# ----------------------------------------------------------------------------
# Copyright (c) 2021-2026 DexForce Technology Co., Ltd.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ----------------------------------------------------------------------------
from __future__ import annotations
import os
import gc
import sys
import queue
import time
import threading
import dexsim
import torch
import numpy as np
import warp as wp
from tqdm import tqdm
from pathlib import Path
from copy import deepcopy
from datetime import datetime
from functools import cached_property
from typing import TYPE_CHECKING, Callable, Dict, List, Sequence, Union
from dataclasses import dataclass, asdict, field, MISSING
# Global cache directories
SIM_CACHE_DIR = Path.home() / ".cache" / "embodichain_cache"
MATERIAL_CACHE_DIR = SIM_CACHE_DIR / "mat_cache"
CONVEX_DECOMP_DIR = SIM_CACHE_DIR / "convex_decomposition"
REACHABLE_XPOS_DIR = SIM_CACHE_DIR / "robot_reachable_xpos"
from dexsim.types import (
Backend,
ThreadMode,
PhysicalAttr,
ActorType,
RigidBodyShape,
RigidBodyGPUAPIReadType,
ArticulationGPUAPIReadType,
)
from dexsim.core import TASK_RETURN
from dexsim.engine import CudaArray, Material
from dexsim.models import MeshObject
from dexsim.render import Light as _Light, LightType, Windows
from dexsim.engine import ObjectManipulator
from embodichain.lab.sim.objects import (
RigidObject,
RigidObjectGroup,
SoftObject,
ClothObject,
Articulation,
Robot,
Light,
RigidConstraint,
)
from embodichain.lab.sim.objects.gizmo import Gizmo, GizmoCfg
from embodichain.lab.sim.sensors import (
SensorCfg,
BaseSensor,
Camera,
CameraCfg,
StereoCamera,
ContactSensor,
)
from embodichain.lab.sim.sensors.attachment import resolve_parent_nodes
from embodichain.lab.sim.cfg import (
RenderCfg,
PhysicsCfg,
MarkerCfg,
GPUMemoryCfg,
WindowRecordCfg,
WindowCameraPoseCfg,
LightCfg,
RigidObjectCfg,
SoftObjectCfg,
ClothObjectCfg,
RigidObjectGroupCfg,
ArticulationCfg,
RobotCfg,
RigidConstraintCfg,
)
from embodichain.lab.sim import VisualMaterial, VisualMaterialCfg
from embodichain.lab.sim.profiler import Profiler, ProfilerCfg
from embodichain.lab.visualization.cfg import VisualizationCfg
from embodichain.utils import configclass, logger
from embodichain.utils.math import look_at_to_pose, matrix_from_quat, pose_inv
if TYPE_CHECKING:
from dexsim.interaction import EntityGizmoConfig, EntityGizmoManipulator
from embodichain.lab.visualization import (
RuntimeHealth,
RuntimeStats,
SceneManifest,
SceneOverlays,
VisualizationRuntime,
)
__all__ = [
"SimulationManager",
"SimulationManagerCfg",
"SIM_CACHE_DIR",
"MATERIAL_CACHE_DIR",
"CONVEX_DECOMP_DIR",
"REACHABLE_XPOS_DIR",
]
[docs]
@configclass
class SimulationManagerCfg:
"""Global robot simulation configuration."""
width: int = 1920
"""The width of the simulation window."""
height: int = 1080
"""The height of the simulation window."""
headless: bool = False
"""Whether to run without an automatically opened native window.
This is forced to ``True`` when the Viser backend is enabled. Viser and
the native DexSim window are mutually exclusive; browser Gizmos do not
require a native window.
"""
enable_entity_gizmo: bool = True
"""Enable native object interaction when the first native window opens.
Headless and Viser simulations do not automatically create native gizmos.
Explicit runtime enable/disable calls take precedence over this default;
reopening a window preserves the current DexSim controller state.
Robot IK interaction is controlled separately by ``robot_ik_gizmo``.
"""
robot_ik_gizmo: GizmoCfg | None = field(default_factory=GizmoCfg)
"""Automatically register robot parts with configured IK chain/TCP metadata.
Native controls activate on the first I press; Viser creates its IK solver
on the first drag and requires ``visualization.allow_commands``. Pure
headless and multi-environment simulations do not register controls.
Defaults to DexSim IK. Use ``GizmoCfg(ik_solver="embodichain")`` to reuse
configured solvers such as Pink, or ``None`` to disable automatic setup.
``GizmoCfg(ik_start_enabled=True)`` explicitly activates native controls on
their first update with an open window, without waiting for I.
"""
render_cfg: RenderCfg = field(default_factory=RenderCfg)
"""The rendering configuration parameters."""
gpu_id: int = 0
"""The gpu index that the simulation engine will be used.
Note: it will affect the gpu physics device if using gpu physics.
"""
thread_mode: ThreadMode = ThreadMode.RENDER_SHARE_ENGINE
"""The threading mode for the simulation engine.
- RENDER_SHARE_ENGINE: The rendering thread shares the same thread with the simulation engine.
- RENDER_SCENE_SHARE_ENGINE: The rendering thread and scene update thread share the same thread with the simulation engine.
"""
cpu_num: int = 1
"""The number of CPU threads to use for the simulation engine."""
num_envs: int = 1
"""The number of parallel environments (arenas) to simulate."""
arena_space: float = 5.0
"""The distance between each arena when building multiple arenas."""
physics_dt: float = 1.0 / 100.0
"""The time step for the physics simulation."""
profiler: ProfilerCfg | None = None
"""Optional simulation profiler. ``None`` disables profiling.
Standalone calls to :meth:`SimulationManager.update` are recorded below a
``sim_update`` root. When the manager is owned by an environment, the same
profiler instance composes with the environment's step/reset hierarchy.
"""
sim_device: Union[str, torch.device] = "cpu"
"""The device for the physics simulation. Can be 'cpu', 'cuda', or a torch.device object."""
physics_config: PhysicsCfg = field(default_factory=PhysicsCfg)
"""The physics configuration parameters."""
gpu_memory_config: GPUMemoryCfg = field(default_factory=GPUMemoryCfg)
"""The GPU memory configuration parameters."""
window_record: WindowRecordCfg = field(default_factory=WindowRecordCfg)
"""Viewer window recording settings (hotkey, paths, FPS, memory budget)."""
window_camera_pose: WindowCameraPoseCfg = field(default_factory=WindowCameraPoseCfg)
"""Interactive viewer camera-pose printing settings."""
visualization: VisualizationCfg = field(default_factory=VisualizationCfg)
"""Live browser visualization settings."""
def __post_init__(self) -> None:
"""Apply visualization-dependent simulation defaults."""
if isinstance(self.robot_ik_gizmo, dict):
self.robot_ik_gizmo = GizmoCfg(**self.robot_ik_gizmo)
if self.robot_ik_gizmo is not None and not isinstance(
self.robot_ik_gizmo, GizmoCfg
):
raise TypeError("robot_ik_gizmo must be a GizmoCfg, mapping, or None.")
if self.visualization.backend == "viser":
self.headless = True
@dataclass
class _WindowRecordState:
"""Internal state for simulation recording."""
time_step: float
max_memory_bytes: int
output_dir: str
video_name: str
save_kwargs: dict[str, object]
record_camera: object | None = None
pose_provider: Callable[[], np.ndarray] | None = None
fixed_pose: np.ndarray | None = None
frames: list[np.ndarray] = field(default_factory=list)
current_memory_bytes: int = 0
last_capture_time: float = field(default_factory=time.time)
accumulated_sim_time: float = 0.0
capture_from_sim_update: bool = False
task_status: int = TASK_RETURN.TASK_LOOP
loop_handle: object | None = None
@dataclass(frozen=True)
class _AxisMarkerGroup:
"""Native axis handles and their backend-neutral display dimensions."""
handles: tuple[MeshObject, ...]
arena_index: int
axis_length: float
axis_radius: float
[docs]
class SimulationManager:
r"""Global Embodied AI simulation manager.
This class is used to manage the global simulation environment and simulated assets.
- assets loading, creation, modification and deletion.
- assets include rigid objects, soft objects, articulations, robots, sensors and lights.
- manager the scenes and the simulation environment.
- parallel scenes simulation on both CPU and GPU.
- create and setup the rendering related settings, eg. environment map, lighting, materials, etc.
- physics simulation management, eg. time step, manual update, etc.
- interactive control via gizmo and window callbacks events.
Args:
sim_config (SimulationManagerCfg, optional): simulation configuration. Defaults to SimulationManagerCfg().
"""
_instances = {}
_cleanup_queue: queue.Queue = queue.Queue()
_DEFAULT_PLANE_GIZMO_TARGET_ID = (1 << 64) - 1
SUPPORTED_SENSOR_TYPES = {
"Camera": Camera,
"StereoCamera": StereoCamera,
"ContactSensor": ContactSensor,
}
[docs]
def __new__(cls, sim_config: SimulationManagerCfg = SimulationManagerCfg()):
"""Create or return the instance based on instance_id."""
n_instance = len(list(cls._instances.keys()))
instance = super(SimulationManager, cls).__new__(cls)
# Store sim_config in the instance for use in __init__ or elsewhere
instance.sim_config = sim_config
instance._is_constructed = False
cls._instances[n_instance] = instance
return instance
[docs]
def __init__(
self, sim_config: SimulationManagerCfg = SimulationManagerCfg()
) -> None:
instance_id = SimulationManager.get_instance_num() - 1
# Mark as initialized
self.instance_id = instance_id
# Cache paths
self._sim_cache_dir = SIM_CACHE_DIR
self._material_cache_dir = MATERIAL_CACHE_DIR
self._convex_decomp_dir = CONVEX_DECOMP_DIR
self._reachable_xpos_dir = REACHABLE_XPOS_DIR
# Setup cache file path.
for path in [
self._sim_cache_dir,
self._material_cache_dir,
self._convex_decomp_dir,
self._reachable_xpos_dir,
]:
os.makedirs(path, exist_ok=True)
self.sim_config = sim_config
self.device = torch.device("cpu")
world_config = self._convert_sim_config(sim_config)
self.profiler = Profiler(sim_config.profiler, self.device)
# Initialize warp runtime context before creating the world.
wp.init()
self._world: dexsim.World = dexsim.World(world_config)
# The caller owns physics time, including while the scene is assembled.
self._world.set_manual_update(True)
self._window: Windows | None = None
self._auto_entity_gizmo_pending = sim_config.enable_entity_gizmo
self._window_record_state: _WindowRecordState | None = None
self._window_record_camera: object | None = None
wr = sim_config.window_record
self._window_record_hotkey_cfg: dict[str, object] | None = (
{
"save_path": wr.save_path,
"fps": wr.fps,
"max_memory": wr.max_memory,
"video_prefix": wr.video_prefix,
}
if wr.enable_hotkey
else None
)
self._window_record_input_control: ObjectManipulator | None = None
self._window_record_save_threads: list[threading.Thread] = []
wcp = sim_config.window_camera_pose
self._window_camera_pose_hotkey_cfg: dict[str, object] | None = (
{"convert_to_look_at": wcp.convert_to_look_at}
if wcp.enable_hotkey
else None
)
self._window_camera_pose_input_control: ObjectManipulator | None = None
self._world.set_delta_time(sim_config.physics_dt)
self._world.show_coordinate_axis(False)
dexsim.set_physics_config(**sim_config.physics_config.to_dexsim_args())
dexsim.set_physics_gpu_memory_config(**sim_config.gpu_memory_config.to_dict())
self._is_initialized_gpu_physics = False
self._ps = self._world.get_physics_scene()
# activate physics
self.enable_physics(True)
self._env = self._world.get_env()
# arena is used as a standalone space for robots to simulate in.
self._arenas: List[dexsim.environment.Arena] = []
# gizmo management
self._gizmos: Dict[str, object] = dict() # Store active gizmos
self._disabled_robot_gizmos: set[str] = set()
# ``(uid, control_part)`` of the Gizmo currently owned by the Viser
# click-to-pick feature, or ``None``. Only one picker Gizmo is kept at a
# time and user-created Gizmos are never touched.
self._picker_gizmo: tuple[str, str | None] | None = None
# marker management
self._markers: dict[str, _AxisMarkerGroup] = {}
self._rigid_objects: Dict[str, RigidObject] = dict()
self._constraints: Dict[str, RigidConstraint] = dict()
self._rigid_object_groups: Dict[str, RigidObjectGroup] = dict()
self._soft_objects: Dict[str, SoftObject] = dict()
self._cloth_objects: Dict[str, ClothObject] = dict()
self._articulations: Dict[str, Articulation] = dict()
self._robots: Dict[str, Robot] = dict()
self._sensors: Dict[str, BaseSensor] = dict()
self._lights: Dict[str, _Light] = dict()
self._visualization_runtime = None
self._visualization_overlays: SceneOverlays | None = None
self._visualization_topology_revision = 0
self._visualization_manifest_topology_revision = -1
self._visualization_sim_step = 0
self._visualization_sim_time = 0.0
self._visualization_error_reported = False
# material placeholder.
self._visual_materials: Dict[str, VisualMaterial] = dict()
# Global texture cache for material creation or randomization.
# The structure is keys to the loaded texture data. The keys represent the texture group.
self._texture_cache: Dict[str, Union[torch.Tensor, List[torch.Tensor]]] = dict()
self._init_sim_resources()
self._create_default_plane()
self.set_default_background()
self.set_default_global_lighting()
self._build_multiple_arenas(sim_config.num_envs)
self.start_visualization()
if sim_config.headless is False:
self._window = self._world.get_windows()
self.is_window_opened = self._window is not None
self._enable_default_entity_gizmo()
self._is_constructed = True
[docs]
@classmethod
def get_instance(cls, instance_id: int = 0) -> SimulationManager:
"""Get the instance of SimulationManager by id.
Args:
instance_id (int): The instance id. Defaults to 0.
Returns:
SimulationManager: The instance.
Raises:
RuntimeError: If the instance has not been created yet.
"""
if instance_id not in cls._instances:
logger.log_error(
f"SimulationManager (id={instance_id}) has not been instantiated yet. "
f"Create an instance first using SimulationManager(sim_config, instance_id={instance_id})."
)
return cls._instances[instance_id]
[docs]
@classmethod
def get_instance_num(cls) -> int:
"""Get the number of instantiated SimulationManager instances.
Returns:
int: The number of instances.
"""
return len(cls._instances)
[docs]
@classmethod
def reset(cls, instance_id: int = 0) -> None:
"""Reset the instance.
This allows creating a new instance with different configuration.
"""
if instance_id in cls._instances:
logger.log_debug(f"Resetting SimulationManager instance {instance_id}.")
del cls._instances[instance_id]
[docs]
@classmethod
def is_instantiated(cls, instance_id: int = 0) -> bool:
"""Check if the instance has been created.
Returns:
bool: True if the instance exists, False otherwise.
"""
return instance_id in cls._instances
[docs]
@classmethod
def set_default_renderer(cls, renderer: str = "auto", gpu_id: int = 0) -> str:
"""Set the global default renderer used by new simulations.
This updates :data:`embodichain.lab.sim.cfg.DEFAULT_RENDERER`, which is
consulted by :func:`embodichain.lab.sim.utility.render_utils.select_default_renderer`
when ``render_cfg.renderer="auto"`` is resolved during :class:`SimulationManager`
construction.
Args:
renderer: The renderer to set. One of ``"auto"``, ``"hybrid"``,
``"fast-rt"``, or ``"rt"``. When ``"auto"``, the renderer is
resolved immediately from the detected GPU via
:func:`embodichain.lab.sim.utility.render_utils.select_default_renderer`.
gpu_id: The CUDA device index to query when ``renderer="auto"``.
Returns:
The resolved renderer name that was set as the default.
"""
from embodichain.lab.sim import cfg
from embodichain.lab.sim.utility.render_utils import select_default_renderer
valid = {"auto", "hybrid", "fast-rt", "rt"}
if renderer not in valid:
logger.log_error(
f"Invalid renderer '{renderer}'. Must be one of {sorted(valid)}."
)
if renderer == "auto":
# Force auto-detection regardless of any previously forced default.
cfg.DEFAULT_RENDERER = "auto"
resolved = select_default_renderer(gpu_id)
else:
resolved = renderer
cfg.DEFAULT_RENDERER = resolved
logger.log_info(f"Default renderer set to '{resolved}'.")
return resolved
@cached_property
def num_envs(self) -> int:
"""Get the number of arenas in the simulation.
Returns:
int: number of arenas.
"""
return len(self._arenas) if len(self._arenas) > 0 else 1
@property
def is_use_gpu_physics(self) -> bool:
"""Check if the physics simulation is using GPU."""
return self.device.type == "cuda"
@property
def asset_uids(self) -> List[str]:
"""Get all assets uid in the simulation.
The assets include lights, sensors, robots, rigid objects and articulations.
Returns:
List[str]: list of all assets uid.
"""
uid_list = ["default_plane"]
uid_list.extend(list(self._lights.keys()))
uid_list.extend(list(self._sensors.keys()))
uid_list.extend(list(self._robots.keys()))
uid_list.extend(list(self._rigid_objects.keys()))
uid_list.extend(list(self._rigid_object_groups.keys()))
uid_list.extend(list(self._soft_objects.keys()))
uid_list.extend(list(self._cloth_objects.keys()))
uid_list.extend(list(self._articulations.keys()))
return uid_list
@property
def visualization_runtime(self) -> VisualizationRuntime | None:
"""Return the active visualization runtime, if one has been started."""
return self._visualization_runtime
@property
def visualization_health(self) -> RuntimeHealth:
"""Return current visualization service and client health."""
from embodichain.lab.visualization import RuntimeHealth
if self._visualization_runtime is not None:
return self._visualization_runtime.health
configured = self.sim_config.visualization.backend == "viser"
return RuntimeHealth(
status="stopped" if configured else "disabled",
running=False,
endpoint=None,
client_count=0,
published_scene_revision=0,
)
@property
def visualization_stats(self) -> RuntimeStats | None:
"""Return visualization telemetry, or ``None`` before startup."""
if self._visualization_runtime is None:
return None
return self._visualization_runtime.stats
@property
def visualization_overlays(self) -> SceneOverlays | None:
"""Return the overlays included in every Viser scene frame."""
return self._visualization_overlays
[docs]
def set_visualization_overlays(self, overlays: SceneOverlays | None) -> None:
"""Set persistent overlays for automatic Viser captures.
The overlays remain active across :meth:`update` calls until replaced
or cleared with ``None``. When Viser is running, the new overlays are
published immediately.
Args:
overlays: Backend-neutral overlays to publish with every frame, or
``None`` to clear all persistent overlays.
"""
self._visualization_overlays = overlays
if (
self.sim_config.visualization.backend == "viser"
and self._visualization_runtime is not None
):
self.capture_visualization_safely(force=True)
[docs]
def notify_visualization_topology_changed(self) -> int:
"""Mark scene topology dirty and return its new local revision."""
self._visualization_topology_revision += 1
return self._visualization_topology_revision
[docs]
def start_visualization(self) -> VisualizationRuntime | None:
"""Start the configured live visualizer and publish the current scene."""
if self.sim_config.visualization.backend == "none":
return None
if getattr(self, "is_window_opened", False):
raise RuntimeError(
"Cannot start the Viser backend while the native DexSim window "
"is open. Close the native window before starting Viser."
)
if self._visualization_runtime is not None:
if self._visualization_runtime.is_running:
return self._visualization_runtime
self._visualization_runtime.stop()
self._visualization_runtime = None
from embodichain.lab.visualization import SceneExporter, VisualizationRuntime
visualization_cfg = self.sim_config.visualization
if (
visualization_cfg.allow_commands
and visualization_cfg.viser_server.host
not in {"127.0.0.1", "localhost", "::1"}
):
logger.log_warning(
"Viser simulation commands are enabled on a non-loopback interface. "
"Only expose this endpoint behind a trusted, authenticated boundary."
)
runtime = VisualizationRuntime(
SceneExporter(self, visualization_cfg),
visualization_cfg,
)
runtime.start()
self._visualization_runtime = runtime
self._visualization_manifest_topology_revision = (
self._visualization_topology_revision
)
self._visualization_error_reported = False
logger.log_info(f"Viser visualization ready at {runtime.endpoint}")
runtime.capture(
sim_step=self._visualization_sim_step,
sim_time=self._visualization_sim_time,
overlays=self._visualization_overlays,
force=True,
)
return runtime
[docs]
def refresh_visualization(self) -> SceneManifest | None:
"""Publish current scene topology when Viser is active."""
runtime = self.start_visualization()
if runtime is None:
return None
for _, gizmo in self.get_gizmo_items():
cancel = getattr(gizmo, "cancel_interaction", None)
if cancel is not None:
cancel("viser:")
manifest = runtime.refresh_scene()
self._visualization_manifest_topology_revision = (
self._visualization_topology_revision
)
return manifest
[docs]
def capture_visualization(
self,
force: bool = False,
*,
capture_camera_images: bool = True,
) -> bool:
"""Capture current scene data for the configured visualizer.
Args:
force: Whether to bypass visualization frame-rate limiting.
capture_camera_images: Whether camera images may be captured.
Returns:
Whether scene or camera data was captured.
"""
runtime = self.start_visualization()
if runtime is None:
return False
if (
self._visualization_manifest_topology_revision
!= self._visualization_topology_revision
):
self.refresh_visualization()
return runtime.capture(
sim_step=self._visualization_sim_step,
sim_time=self._visualization_sim_time,
overlays=self._visualization_overlays,
force=force,
capture_camera_images=capture_camera_images,
)
[docs]
def capture_visualization_safely(
self,
force: bool = False,
*,
capture_camera_images: bool = True,
) -> None:
"""Update visualization without allowing failures to stop simulation.
The first visualization failure is logged and subsequent captures are
skipped until the runtime is restarted.
Args:
force: Whether to bypass visualization frame-rate limiting.
capture_camera_images: Whether camera images may be captured.
"""
if self._visualization_error_reported:
return
try:
self.capture_visualization(
force=force,
capture_camera_images=capture_camera_images,
)
except Exception as error:
if not self._visualization_error_reported:
logger.log_warning(f"Viser visualization update failed: {error!r}")
self._visualization_error_reported = True
[docs]
def stop_visualization(self) -> None:
"""Stop the visualization server and release its worker thread."""
runtime = self._visualization_runtime
if runtime is None:
return
try:
runtime.stop()
finally:
if getattr(self, "_picker_gizmo", None) is not None:
self._release_picker_gizmo()
for _, gizmo in self.get_gizmo_items():
cancel = getattr(gizmo, "cancel_interaction", None)
if cancel is not None:
cancel("viser:")
self._visualization_runtime = None
def _convert_sim_config(
self, sim_config: SimulationManagerCfg
) -> dexsim.WorldConfig:
world_config = dexsim.WorldConfig()
win_config = dexsim.WindowsConfig()
win_config.width = sim_config.width
win_config.height = sim_config.height
world_config.cpu_num = sim_config.cpu_num
world_config.win_config = win_config
world_config.open_windows = not sim_config.headless
self.is_window_opened = not sim_config.headless
world_config.backend = Backend.VULKAN
world_config.thread_mode = sim_config.thread_mode
world_config.cache_path = str(self._material_cache_dir)
world_config.length_tolerance = sim_config.physics_config.length_tolerance
world_config.speed_tolerance = sim_config.physics_config.speed_tolerance
if sim_config.render_cfg.renderer == "auto":
from embodichain.lab.sim.utility.render_utils import (
select_default_renderer,
)
resolved_renderer = select_default_renderer(sim_config.gpu_id)
logger.log_info(
f"Auto-selected '{resolved_renderer}' renderer for gpu_id={sim_config.gpu_id}."
)
sim_config.render_cfg.renderer = resolved_renderer
sim_config.render_cfg.apply_to_dexsim_config(world_config)
if type(sim_config.sim_device) is str:
self.device = torch.device(sim_config.sim_device)
else:
self.device = sim_config.sim_device
if self.device.type == "cuda":
world_config.enable_gpu_sim = True
world_config.direct_gpu_api = True
if self.device.index is not None and sim_config.gpu_id != self.device.index:
logger.log_warning(
f"Conflict gpu_id {sim_config.gpu_id} and device index {self.device.index}. Using device index."
)
sim_config.gpu_id = self.device.index
self.device = torch.device(f"cuda:{sim_config.gpu_id}")
world_config.gpu_id = sim_config.gpu_id
return world_config
def _init_sim_resources(self) -> None:
"""Initialize the default simulation resources."""
from embodichain.data.assets import SimResources
self._default_resources = SimResources()
[docs]
def enable_physics(self, enable: bool) -> None:
"""Enable or disable physics simulation.
Args:
enable (bool): whether to enable physics simulation.
"""
self._world.enable_physics(enable)
[docs]
def init_gpu_physics(self) -> None:
"""Initialize the GPU physics simulation."""
if self.device.type != "cuda":
logger.log_warning(
"The simulation device is not cuda, cannot initialize GPU physics."
)
return
if self._is_initialized_gpu_physics:
return
for art in self._articulations.values():
art.reallocate_body_data()
for robot in self._robots.values():
robot.reallocate_body_data()
# Re-establish rigid object positions after articulation resets, ensuring
# no articulation kinematics step has inadvertently corrupted the broadphase
# state for rigid bodies.
for rigid_obj in self._rigid_objects.values():
rigid_obj.reset()
self._is_initialized_gpu_physics = True
[docs]
def render_camera_group(self, group_ids: list[int]) -> None:
"""Render all camera group in the simulation.
Args:
group_ids (list[int]): The list of camera group ids to render.
Note: This interface is only valid when Ray Tracing rendering backend is enabled.
"""
self._world.render_camera_group(group_ids)
[docs]
def update(self, physics_dt: float | None = None, step: int = 10) -> None:
"""Advance physics explicitly and publish the resulting simulation state.
Each substep applies pending Gizmo controls before advancing the world,
then updates simulation time, recording, and browser visualization.
Physics does not advance in the background while the caller is idle.
Args:
physics_dt (float | None, optional): the time step for physics simulation. Defaults to None.
step (int, optional): the number of steps to update physics. Defaults to 10.
"""
with self.profiler.section("sim_update", is_root=True):
with self.profiler.section("gpu_physics_check"):
if self.is_use_gpu_physics and not self._is_initialized_gpu_physics:
logger.log_warning(
"Using GPU physics, but not initialized yet. "
"Forcing initialization."
)
with self.profiler.section("gpu_physics_init"):
self.init_gpu_physics()
with self.profiler.section("physics_steps"):
if physics_dt is None:
with self.profiler.section("resolve_physics_dt"):
physics_dt = self.sim_config.physics_dt
for i in range(step):
with self.profiler.section("gizmo_update"):
self.update_gizmos()
with self.profiler.section("world_update"):
self._world.update(physics_dt)
self._visualization_sim_step += 1
self._visualization_sim_time += physics_dt
if (
self._window_record_state is not None
and self._window_record_state.capture_from_sim_update
):
with self.profiler.section("window_record_capture"):
self._step_window_record_from_sim_update(
self._window_record_state, physics_dt
)
if self.sim_config.visualization.backend == "viser":
with self.profiler.section("visualization_capture"):
self.capture_visualization_safely(
capture_camera_images=i == step - 1
)
[docs]
def get_env(self, arena_index: int = -1) -> dexsim.environment.Arena:
"""Get the arena or env by index.
If arena_index is -1, return the global env.
If arena_index is valid, return the corresponding arena.
Args:
arena_index (int, optional): the index of arena to get, -1 for global env. Defaults to -1.
Returns:
dexsim.environment.Arena: The arena or global env.
"""
if arena_index >= 0:
if arena_index > len(self._arenas) - 1:
logger.log_error(
f"Invalid arena index: {arena_index}. Current number of arenas: {len(self._arenas)}"
)
return self._arenas[arena_index]
else:
return self._env
[docs]
def visualize_point_cloud(
self,
points: torch.Tensor | np.ndarray,
colors: torch.Tensor | np.ndarray | None = None,
point_size: float = 2.0,
name: str = "point_cloud",
) -> dexsim.models.PointCloud:
"""Visualize a static point cloud in the native simulation viewer.
Each invocation creates a separate native point-cloud object. This
convenience API is intended for static data, not incremental or
streaming updates.
Args:
points: Point positions with shape ``(N, 3)``.
colors: Optional per-point RGB or RGBA colors with shape ``(N, 3)``
or ``(N, 4)``. Values in ``[0, 255]`` are normalized to
``[0, 1]``. The alpha channel of RGBA input is ignored by the
native renderer. Defaults to green.
point_size: Native renderer point size. Defaults to ``2.0``.
name: Name assigned to the native point-cloud object.
Returns:
The native DexSim point-cloud handle.
Raises:
RuntimeError: If there is no active simulation environment.
ValueError: If the points or colors do not have a supported shape.
"""
if isinstance(points, torch.Tensor):
points = points.detach().cpu().numpy()
points = np.asarray(points, dtype=np.float32)
if points.ndim != 2 or points.shape[1] != 3:
raise ValueError(f"Points must have shape (N, 3), got {points.shape}")
if len(points) == 0:
raise ValueError("Points array is empty")
if colors is None:
colors = np.tile(
np.array((0.0, 1.0, 0.0), dtype=np.float32), (len(points), 1)
)
else:
if isinstance(colors, torch.Tensor):
colors = colors.detach().cpu().numpy()
colors = np.asarray(colors)
if colors.ndim != 2 or colors.shape[0] != len(points):
raise ValueError(
f"Colors must have shape ({len(points)}, 3) or ({len(points)}, 4), "
f"got {colors.shape}"
)
if colors.shape[1] not in (3, 4):
raise ValueError(
"Colors must have 3 (RGB) or 4 (RGBA) channels, "
f"got {colors.shape[1]}"
)
if colors.max() > 1.0:
colors = colors / 255.0
colors = np.asarray(colors[:, :3], dtype=np.float32)
env = self.get_env()
if env is None:
raise RuntimeError("Simulation manager has no active simulation")
point_cloud = env.create_point_cloud(name=name)
point_cloud.add_points(points)
point_cloud.set_colors(colors)
point_cloud.set_point_size(point_size)
logger.log_info(
f"Created point cloud '{name}' with {len(points)} points "
f"(point_size={point_size})"
)
return point_cloud
[docs]
def get_world(self) -> dexsim.World:
return self._world
[docs]
def can_open_native_window(self) -> bool:
"""Return whether the native DexSim window may be opened.
The Viser backend owns visualization while it is configured or
running, so a native window must not be opened for the same simulation.
Returns:
``True`` unless the Viser backend is configured or running.
"""
return (
self.sim_config.visualization.backend != "viser"
and self._visualization_runtime is None
)
[docs]
def open_window(self) -> bool:
"""Open the native DexSim simulation window when allowed.
Viser owns visualization while it is configured or running. In that
case this method safely skips the native window so launchers do not
need a separate Viser condition.
Returns:
``True`` when the native window is open, otherwise ``False``.
"""
if not self.can_open_native_window():
logger.log_info(
"Skipping the native DexSim window because the Viser backend "
"is configured or running."
)
return False
if self.is_window_opened:
return True
self._world.open_window()
self._window = self._world.get_windows()
if self._window is None:
return False
self._enable_default_entity_gizmo()
if (
self._window_record_hotkey_cfg is not None
and self._window_record_input_control is None
):
self.enable_window_record_hotkey(**self._window_record_hotkey_cfg)
if (
self._window_camera_pose_hotkey_cfg is not None
and self._window_camera_pose_input_control is None
):
self.enable_window_camera_pose_hotkey(**self._window_camera_pose_hotkey_cfg)
self.is_window_opened = True
return True
[docs]
def close_window(self) -> None:
"""Close the simulation window."""
if self.is_window_recording():
self.stop_window_record()
for _, gizmo in self.get_gizmo_items():
gizmo.detach_native_window()
self._world.close_window()
self._window = None
self._window_record_input_control = None
self._window_camera_pose_input_control = None
self.is_window_opened = False
def _build_multiple_arenas(self, num: int, space: float | None = None) -> None:
"""Build multiple arenas in a grid pattern.
This interface is used for vectorized simulation.
Args:
num (int): number of arenas to build.
space (float | None, optional): The distance between each arena. Defaults to the arena_space in sim_config.
"""
if space is None:
space = self.sim_config.arena_space
if num <= 0:
logger.log_warning("Number of arenas must be greater than 0.")
return
scene_grid_length = int(np.ceil(np.sqrt(num)))
for i in range(num):
arena = self._env.add_arena(f"arena_{i}")
id_x, id_y = i % scene_grid_length, i // scene_grid_length
arena.set_root_node_position([id_x * space, id_y * space, 0])
self._arenas.append(arena)
[docs]
def set_indirect_lighting(self, name: str) -> None:
"""Set indirect lighting.
Args:
name (str): name of path of the indirect lighting.
"""
if name.startswith("/") is False:
ibl_path = self._default_resources.get_ibl_path(name)
logger.log_info(f"Set IBL {name} from sim default resources.")
else:
ibl_path = name
logger.log_info(f"Set IBL {name} from custom path.")
self._env.set_IBL(ibl_path)
[docs]
def set_emission_light(
self, color: Sequence[float] | None = None, intensity: float | None = None
) -> None:
"""Set environment emission light.
Args:
color (Sequence[float] | None): color of the light.
intensity (float | None): intensity of the light.
"""
if color is not None:
self._env.set_env_light_emission(color)
if intensity is not None:
self._env.set_env_light_intensity(intensity)
def _create_default_plane(self):
default_length = 1000
repeat_uv_size = int(default_length / 2)
self._default_plane = self._env.create_plane(
0, default_length, repeat_uv_size, repeat_uv_size
)
self._default_plane.set_name("default_plane")
plane_collision = self._env.create_cube(
default_length, default_length, default_length / 10
)
plane_collision.set_visible(False)
plane_collision_pose = np.eye(4, dtype=float)
plane_collision_pose[2, 3] = -default_length / 20 - 0.001
plane_collision.set_local_pose(plane_collision_pose)
plane_collision.add_rigidbody(ActorType.KINEMATIC, RigidBodyShape.CONVEX)
# TODO: add default physics attributes for the plane.
[docs]
def set_default_global_lighting(self) -> None:
"""Set default global lighting for the scene.
Configures both the environment emission (ambient) light and a
directional light to provide default scene illumination. The
directional light is a global scene light (infinite distance)
pointing downward along the -Z axis.
"""
# Environment emission light
self.set_emission_light([1.0, 1.0, 1.0], 100.0)
[docs]
def set_default_background(self) -> None:
"""Set default background."""
mat_name = "plane_mat"
mat = None
mat_path = self._default_resources.get_material_path("PlaneDark")
color_texture = os.path.join(mat_path, "PlaneDark_2K_Color.jpg")
roughness_texture = os.path.join(mat_path, "PlaneDark_2K_Roughness.jpg")
mat = self.create_visual_material(
cfg=VisualMaterialCfg(
uid=mat_name,
base_color_texture=color_texture,
roughness_texture=roughness_texture,
roughness=1.0,
)
)
self._default_plane.set_material(mat.get_instance("plane_mat").mat)
self._visual_materials[mat_name] = mat
[docs]
def set_ground_plane_visibility(self, visible: bool) -> None:
"""_summary_
Args:
visible (bool): _description_
"""
if visible:
self._default_plane.set_visible(True)
else:
self._default_plane.set_visible(False)
[docs]
def set_texture_cache(
self, key: str, texture: Union[torch.Tensor, List[torch.Tensor]]
) -> None:
"""Set the texture to the global texture cache.
Args:
key (str): The key of the texture.
texture (Union[torch.Tensor, List[torch.Tensor]]): The texture data.
"""
self._texture_cache[key] = texture
[docs]
def get_texture_cache(
self, key: str | None = None
) -> torch.Tensor | list[torch.Tensor] | None:
"""Get the texture from the global texture cache.
Args:
key (str | None, optional): The key of the texture. If None, return None. Defaults to None.
Returns:
torch.Tensor | list[torch.Tensor] | None: The texture if found, otherwise None.
"""
if key is None:
return self._texture_cache
if key not in self._texture_cache:
logger.log_warning(f"Texture {key} not found in global texture cache.")
return None
return self._texture_cache[key]
[docs]
def get_asset(
self, uid: str
) -> Light | BaseSensor | Robot | RigidObject | Articulation | None:
"""Get an asset by its UID.
The asset can be a light, sensor, robot, rigid object or articulation.
Args:
uid (str): The UID of the asset.
Returns:
Light | BaseSensor | Robot | RigidObject | Articulation | None: The asset instance if found, otherwise None.
"""
if uid in self._lights:
return self._lights[uid]
if uid in self._sensors:
return self._sensors[uid]
if uid in self._robots:
return self._robots[uid]
if uid in self._rigid_objects:
return self._rigid_objects[uid]
if uid in self._rigid_object_groups:
return self._rigid_object_groups[uid]
if uid in self._soft_objects:
return self._soft_objects[uid]
if uid in self._cloth_objects:
return self._cloth_objects[uid]
if uid in self._articulations:
return self._articulations[uid]
logger.log_warning(f"Asset {uid} not found.")
return None
# Light type string → dexsim LightType enum mapping
_LIGHT_TYPE_MAP: dict[str, LightType] = {
"point": LightType.POINT,
"sun": LightType.SUN,
"direction": LightType.DIRECTION,
"spot": LightType.SPOT,
"rect": LightType.RECT,
"mesh": LightType.MESH,
}
# Light types that are created as a single global scene light (not per-environment).
_GLOBAL_LIGHT_TYPES: tuple[str, ...] = ("sun", "direction")
[docs]
def add_light(self, cfg: LightCfg) -> Light:
"""Create a light in the scene.
Supports six light types: ``"point"``, ``"sun"``, ``"direction"``,
``"spot"``, ``"rect"``, and ``"mesh"``. See :class:`LightCfg` for
type-specific configuration fields.
.. attention::
``"sun"`` and ``"direction"`` lights are global scene lights
(infinite-distance directional light sources). They are created
as a single instance on the root environment, not batched per
environment. All other types are created as per-environment
batched lights.
Args:
cfg (LightCfg): Configuration for the light, including type, color,
intensity, and type-specific properties.
Returns:
Light: The created light instance.
Raises:
RuntimeError: If ``cfg.light_type`` is not one of the supported types.
"""
if cfg.uid is None:
uid = "light"
cfg.uid = uid
else:
uid = cfg.uid
if uid in self._lights:
logger.log_error(f"Light {uid} already exists.")
light_type_str = cfg.light_type
light_type = self._LIGHT_TYPE_MAP.get(light_type_str)
if light_type is None:
supported = ", ".join(self._LIGHT_TYPE_MAP.keys())
logger.log_error(
f"Unsupported light type: '{light_type_str}'. "
f"Supported types: {supported}."
)
# Validation warnings for type-specific constraints
if light_type_str == "mesh" and not cfg.mesh_path:
logger.log_warning(
f"Mesh light '{uid}' has no mesh_path set. "
f"Use set_mesh() to assign a MeshObject."
)
if light_type_str == "rect" and (cfg.rect_width <= 0 or cfg.rect_height <= 0):
logger.log_warning(
f"Rect light '{uid}' has zero or negative dimensions "
f"(width={cfg.rect_width}, height={cfg.rect_height})."
)
if cfg.light_type in self._GLOBAL_LIGHT_TYPES:
# Global scene light: create a single instance on the root
# environment. Infinite-distance lights (sun, direction) are
# physically scene-global and should not be duplicated per arena.
light = self._env.create_light(uid, light_type)
batch_lights = Light(cfg=cfg, entities=[light])
else:
# Per-environment batched light: one instance per arena.
env_list = [self._env] if len(self._arenas) == 0 else self._arenas
light_list = []
for i, env in enumerate(env_list):
light_name = f"{uid}_{i}"
light = env.create_light(light_name, light_type)
light_list.append(light)
batch_lights = Light(cfg=cfg, entities=light_list)
self._lights[uid] = batch_lights
return batch_lights
[docs]
def get_light(self, uid: str) -> Light | None:
"""Get a light by its UID.
Args:
uid (str): The UID of the light.
Returns:
Light | None: The light instance if found, otherwise None.
"""
if uid not in self._lights:
logger.log_warning(f"Light {uid} not found.")
return None
return self._lights[uid]
[docs]
def get_light_uid_list(self) -> List[str]:
"""Get current light uid list
Returns:
List[str]: list of light uid.
"""
return list(self._lights.keys())
[docs]
def add_rigid_object(
self,
cfg: RigidObjectCfg,
) -> RigidObject:
"""Add a rigid object to the scene.
Args:
cfg (RigidObjectCfg): Configuration for the rigid object.
Returns:
RigidObject: The added rigid object instance handle.
"""
from embodichain.lab.sim.utility.sim_utils import (
load_mesh_objects_from_cfg,
)
uid = cfg.uid
if uid is None:
logger.log_error("Rigid object uid must be specified.")
if uid in self._rigid_objects:
logger.log_error(f"Rigid object {uid} already exists.")
env_list = [self._env] if len(self._arenas) == 0 else self._arenas
obj_list = load_mesh_objects_from_cfg(
cfg=cfg,
env_list=env_list,
cache_dir=self._convex_decomp_dir,
)
rigid_obj = RigidObject(cfg=cfg, entities=obj_list, device=self.device)
if cfg.shape.visual_material:
mat = self.create_visual_material(cfg.shape.visual_material)
rigid_obj.set_visual_material(mat, update_default=True)
self._rigid_objects[uid] = rigid_obj
self.notify_visualization_topology_changed()
return rigid_obj
[docs]
def add_soft_object(self, cfg: SoftObjectCfg) -> SoftObject:
"""Add a soft object to the scene.
Args:
cfg (SoftObjectCfg): Configuration for the soft object.
Returns:
SoftObject: The added soft object instance handle.
"""
if not self.is_use_gpu_physics:
logger.log_error("Soft object requires GPU physics to be enabled.")
from embodichain.lab.sim.utility import (
load_soft_object_from_cfg,
)
uid = cfg.uid
if uid is None:
logger.log_error("Soft object uid must be specified.")
env_list = [self._env] if len(self._arenas) == 0 else self._arenas
obj_list = load_soft_object_from_cfg(
cfg=cfg,
env_list=env_list,
)
soft_obj = SoftObject(cfg=cfg, entities=obj_list, device=self.device)
self._soft_objects[uid] = soft_obj
self.notify_visualization_topology_changed()
return soft_obj
[docs]
def add_cloth_object(self, cfg: ClothObjectCfg) -> ClothObject:
"""Add a cloth object to the scene.
Args:
cfg (ClothObjectCfg): Configuration for the cloth object.
Returns:
ClothObject: The added cloth object instance handle.
"""
if not self.is_use_gpu_physics:
logger.log_error("Cloth object requires GPU physics to be enabled.")
from embodichain.lab.sim.utility import (
load_cloth_object_from_cfg,
)
uid = cfg.uid
if uid is None:
logger.log_error("Cloth object uid must be specified.")
env_list = [self._env] if len(self._arenas) == 0 else self._arenas
obj_list = load_cloth_object_from_cfg(
cfg=cfg,
env_list=env_list,
)
cloth_obj = ClothObject(cfg=cfg, entities=obj_list, device=self.device)
self._cloth_objects[uid] = cloth_obj
self.notify_visualization_topology_changed()
return cloth_obj
[docs]
def get_rigid_object(self, uid: str) -> RigidObject | None:
"""Get a rigid object by its unique ID.
Args:
uid (str): The unique ID of the rigid object.
Returns:
RigidObject | None: The rigid object instance if found, otherwise None.
"""
if uid not in self._rigid_objects:
logger.log_warning(f"Rigid object {uid} not found.")
return None
return self._rigid_objects[uid]
[docs]
def get_soft_object(self, uid: str) -> SoftObject | None:
"""Get a soft object by its unique ID.
Args:
uid (str): The unique ID of the soft object.
Returns:
SoftObject | None: The soft object instance if found, otherwise None.
"""
if uid not in self._soft_objects:
logger.log_warning(f"Soft object {uid} not found.")
return None
return self._soft_objects[uid]
[docs]
def get_cloth_object(self, uid: str) -> ClothObject | None:
"""Get a cloth object by its unique ID.
Args:
uid (str): The unique ID of the cloth object.
Returns:
ClothObject | None: The cloth object instance if found, otherwise None.
"""
if uid not in self._cloth_objects:
logger.log_warning(f"Cloth object {uid} not found.")
return None
return self._cloth_objects[uid]
[docs]
def get_rigid_object_uid_list(self) -> List[str]:
"""Get current rigid body uid list
Returns:
List[str]: list of rigid body uid.
"""
return list(self._rigid_objects.keys())
@staticmethod
def _broadcast_frame(
frame: np.ndarray | None,
num_envs: int,
env_ids: Sequence[int],
name: str,
) -> list[np.ndarray]:
"""Broadcast a local-frame spec to one matrix per target env.
Args:
frame: None -> identity; (4,4) -> repeated; (N,4,4) -> indexed per env.
num_envs: Total number of arenas (used to validate (N,4,4)).
env_ids: Target env indices to produce frames for.
name: Constraint name (for error messages).
Returns:
A list of (4,4) numpy arrays, one per env in env_ids.
Raises:
RuntimeError: If an (N,4,4) frame's N != num_envs, or shape is invalid.
"""
if frame is None:
identity = np.eye(4, dtype=np.float32)
return [identity for _ in env_ids]
frame_np = np.asarray(frame, dtype=np.float32)
if frame_np.shape == (4, 4):
return [frame_np for _ in env_ids]
if frame_np.ndim == 3 and frame_np.shape[1:] == (4, 4):
if frame_np.shape[0] != num_envs:
logger.log_error(
f"Constraint '{name}' local frame has shape {frame_np.shape} "
f"but num_envs is {num_envs}. Expected ({num_envs}, 4, 4)."
)
return [frame_np[i] for i in env_ids]
logger.log_error(
f"Constraint '{name}' local frame has invalid shape {frame_np.shape}. "
"Expected None, (4, 4), or (N, 4, 4)."
)
@staticmethod
def _normalize_env_ids(
env_ids: Sequence[int] | torch.Tensor | None,
num_envs: int,
) -> list[int]:
"""Normalize an ``env_ids`` spec to a plain ``list[int]``.
Accepts ``None`` (-> all envs), a ``torch.Tensor`` (as passed by the
:class:`EventManager`), or any ``Sequence[int]``, and returns a list of
Python ints. Normalizing here keeps the per-arena constraint names clean
(e.g. ``"weld_0"`` rather than relying on a tensor's string form) and
avoids depending on implicit tensor-to-int conversions downstream.
Args:
env_ids: None, a tensor, or a sequence of ints.
num_envs: Total number of arenas (used when env_ids is None).
Returns:
A list of int env indices.
"""
if env_ids is None:
return list(range(num_envs))
if isinstance(env_ids, torch.Tensor):
return env_ids.detach().cpu().tolist()
return [int(i) for i in env_ids]
[docs]
def create_rigid_constraint(
self,
cfg: RigidConstraintCfg,
env_ids: Sequence[int] | torch.Tensor | None = None,
) -> RigidConstraint:
"""Create a fixed constraint between two RigidObjects.
Binds ``rigid_object_a``'s entity[i] to ``rigid_object_b``'s entity[i]
within arena[i], for each env in ``env_ids``. Local frames default to
welding the objects at their *current* relative pose:
``local_frame_a`` defaults to identity (object A's origin) and
``local_frame_b`` defaults to ``inv(pose_B) @ pose_A`` (computed per env),
so the offset is preserved rather than the two origins being pulled
together. Pass explicit frames to define a specific joint frame.
Args:
cfg: The constraint configuration.
env_ids: Target environment indices. Accepts a tensor (as passed by
the :class:`EventManager`) or a sequence of ints. None -> all arenas.
Returns:
The created :class:`RigidConstraint`.
Raises:
RuntimeError: If either object is missing, the name is already in use,
a frame shape is invalid, or dexsim fails to create a handle.
"""
# validate constraint type (only fixed supported in v1)
if cfg.constraint_type != "fixed":
logger.log_error(
f"Constraint '{cfg.name}' has unsupported type "
f"'{cfg.constraint_type}'. Only 'fixed' is supported in v1."
)
# resolve objects
if cfg.rigid_object_a_uid not in self._rigid_objects:
logger.log_error(
f"RigidObject '{cfg.rigid_object_a_uid}' not found for constraint "
f"'{cfg.name}'. Available: {list(self._rigid_objects.keys())}."
)
if cfg.rigid_object_b_uid not in self._rigid_objects:
logger.log_error(
f"RigidObject '{cfg.rigid_object_b_uid}' not found for constraint "
f"'{cfg.name}'. Available: {list(self._rigid_objects.keys())}."
)
rigid_object_a = self._rigid_objects[cfg.rigid_object_a_uid]
rigid_object_b = self._rigid_objects[cfg.rigid_object_b_uid]
# validate duplicate name
if cfg.name in self._constraints:
logger.log_error(
f"Constraint '{cfg.name}' already exists. Remove it before recreating."
)
# validate object entity counts match num_envs
num_envs = self.num_envs
if rigid_object_a.num_instances != num_envs:
logger.log_error(
f"RigidObject '{cfg.rigid_object_a_uid}' has "
f"{rigid_object_a.num_instances} instances but num_envs is {num_envs}."
)
if rigid_object_b.num_instances != num_envs:
logger.log_error(
f"RigidObject '{cfg.rigid_object_b_uid}' has "
f"{rigid_object_b.num_instances} instances but num_envs is {num_envs}."
)
# resolve target env_ids (accepts None / tensor / sequence)
target_env_ids = self._normalize_env_ids(env_ids, num_envs)
# broadcast local frames.
# local_frame_a defaults to identity (object A's origin).
# local_frame_b defaults to the current relative pose of A w.r.t. B
# (inv(pose_B) @ pose_A), so that with both frames left as None the
# constraint welds the objects at their *current* relative pose instead
# of pulling their origins together.
frames_a = self._broadcast_frame(
cfg.local_frame_a, num_envs, target_env_ids, cfg.name
)
if cfg.local_frame_b is None:
pose_a = rigid_object_a.get_local_pose(to_matrix=True)
pose_b = rigid_object_b.get_local_pose(to_matrix=True)
frame_b = torch.bmm(pose_inv(pose_b), pose_a) # (N, 4, 4)
frame_b = frame_b.cpu().numpy().astype(np.float32)
frames_b = [frame_b[i] for i in target_env_ids]
else:
frames_b = self._broadcast_frame(
cfg.local_frame_b, num_envs, target_env_ids, cfg.name
)
# pre-size handles list with None, fill target envs
handles: list = [None] * num_envs
try:
for idx, env_id in enumerate(target_env_ids):
arena = self.get_env(env_id)
name_i = cfg.name if num_envs <= 1 else f"{cfg.name}_{env_id}"
handle = arena.create_fixed_constraint(
name_i,
rigid_object_a._entities[env_id],
rigid_object_b._entities[env_id],
frames_a[idx],
frames_b[idx],
)
if handle is None:
logger.log_error(
f"Failed to create constraint '{name_i}' in arena {env_id}."
)
handles[env_id] = handle
except Exception:
# Ensure partially created per-arena constraints are removed if a later
# arena fails, so create/remove semantics stay consistent.
RigidConstraint(
cfg=cfg,
constraint_handles=handles,
rigid_object_a=rigid_object_a,
rigid_object_b=rigid_object_b,
device=self.device,
).destroy(env_ids=target_env_ids, arena_resolver=self.get_env)
raise
constraint = RigidConstraint(
cfg=cfg,
constraint_handles=handles,
rigid_object_a=rigid_object_a,
rigid_object_b=rigid_object_b,
device=self.device,
)
self._constraints[cfg.name] = constraint
return constraint
[docs]
def get_soft_object_uid_list(self) -> List[str]:
"""Get current soft body uid list
Returns:
List[str]: list of soft body uid.
"""
return list(self._soft_objects.keys())
[docs]
def get_cloth_object_uid_list(self) -> List[str]:
"""Get current cloth body uid list
Returns:
List[str]: list of cloth body uid.
"""
return list(self._cloth_objects.keys())
[docs]
def remove_rigid_constraint(
self,
name: str,
env_ids: Sequence[int] | torch.Tensor | None = None,
) -> bool:
"""Remove a rigid constraint by name.
With ``env_ids=None`` the constraint is removed from every arena and
dropped from the registry. With a subset, only those arenas are cleared;
the registry entry is kept until all handles become None.
Args:
name: The base constraint name.
env_ids: Subset of arenas to clear. Accepts a tensor (as passed by
the :class:`EventManager`) or a sequence of ints. None -> all.
Returns:
True if the constraint was found (and removed or partially removed),
False if the name is unknown.
"""
constraint = self._constraints.get(name, None)
if constraint is None:
logger.log_warning(f"Constraint '{name}' not found. Nothing to remove.")
return False
target_env_ids = self._normalize_env_ids(env_ids, constraint.num_envs)
constraint.destroy(env_ids=target_env_ids, arena_resolver=self.get_env)
# drop from registry if no handles remain active
if all(h is None for h in constraint.constraint_handles):
del self._constraints[name]
return True
[docs]
def get_rigid_constraint(self, name: str) -> RigidConstraint | None:
"""Get a rigid constraint by its base name.
Args:
name: The base constraint name.
Returns:
The constraint, or None if not found.
"""
if name not in self._constraints:
logger.log_warning(f"Constraint '{name}' not found.")
return None
return self._constraints[name]
[docs]
def get_rigid_constraint_uid_list(self) -> List[str]:
"""Get the list of registered constraint base names.
Returns:
List[str]: list of constraint names.
"""
return list(self._constraints.keys())
[docs]
def add_rigid_object_group(self, cfg: RigidObjectGroupCfg) -> RigidObjectGroup:
"""Add a rigid object group to the scene.
Args:
cfg (RigidObjectGroupCfg): Configuration for the rigid object group.
"""
from embodichain.lab.sim.utility.sim_utils import (
load_mesh_objects_from_cfg,
)
uid = cfg.uid
if uid is None:
logger.log_error("Rigid object group uid must be specified.")
if uid in self._rigid_object_groups:
logger.log_error(f"Rigid object group {uid} already exists.")
if cfg.body_type == "static":
logger.log_error("Rigid object group cannot be static.")
env_list = [self._env] if len(self._arenas) == 0 else self._arenas
obj_group_list = []
for key, rigid_cfg in tqdm(
cfg.rigid_objects.items(), desc="Loading rigid objects"
):
obj_list = load_mesh_objects_from_cfg(
cfg=rigid_cfg,
env_list=env_list,
cache_dir=self._convex_decomp_dir,
)
obj_group_list.append(obj_list)
# Convert [a1, a2, ...], [b1, b2, ...] to [(a1, b1, ...), (a2, b2, ...), ...]
obj_group_list = list(zip(*obj_group_list))
rigid_obj_group = RigidObjectGroup(
cfg=cfg, entities=obj_group_list, device=self.device
)
self._rigid_object_groups[uid] = rigid_obj_group
self.notify_visualization_topology_changed()
return rigid_obj_group
[docs]
def get_rigid_object_group(self, uid: str) -> RigidObjectGroup | None:
"""Get a rigid object group by its unique ID.
Args:
uid (str): The unique ID of the rigid object group.
Returns:
RigidObjectGroup | None: The rigid object group instance if found, otherwise None.
"""
if uid not in self._rigid_object_groups:
logger.log_warning(f"Rigid object group {uid} not found.")
return None
return self._rigid_object_groups[uid]
[docs]
def get_rigid_object_group_uid_list(self) -> List[str]:
"""Get current rigid body group uid list
Returns:
List[str]: list of rigid body group uid.
"""
return list(self._rigid_object_groups.keys())
@cached_property
def arena_offsets(self) -> torch.Tensor:
"""Get the arena offsets for all arenas.
Returns:
torch.Tensor: The arena offsets of shape (num_arenas, 3).
"""
env_list = [self._env] if len(self._arenas) == 0 else self._arenas
arena_offsets = torch.zeros(
(len(env_list), 3), dtype=torch.float32, device=self.device
)
for i, env in enumerate(env_list):
arena_position = env.get_root_node().get_world_pose()[:3, 3]
arena_offsets[i] = torch.tensor(
arena_position, dtype=torch.float32, device=self.device
)
return arena_offsets
[docs]
def has_non_static_rigid_object(self) -> bool:
"""Check if there is any non-static rigid object in the simulation.
Returns:
bool: True if there is at least one non-static rigid object, False otherwise.
"""
for rigid_obj in self._rigid_objects.values():
if rigid_obj.body_type != "static":
return True
if len(self._rigid_object_groups) > 0:
return True
return False
[docs]
def add_articulation(
self,
cfg: ArticulationCfg,
) -> Articulation:
"""Add an articulation to the scene.
Args:
cfg (ArticulationCfg): Configuration for the articulation.
Returns:
Articulation: The added articulation instance handle.
"""
uid = cfg.uid
if uid is None:
uid = os.path.splitext(os.path.basename(cfg.fpath))[0]
cfg.uid = uid
if uid in self._articulations:
logger.log_error(f"Articulation {uid} already exists.")
env_list = [self._env] if len(self._arenas) == 0 else self._arenas
obj_list = []
is_usd = cfg.fpath.endswith((".usd", ".usda", ".usdc"))
if is_usd:
# TODO: Currently add checking for num_envs when file is USD. After we support spawn via cloning, we can remove this.
if len(env_list) > 1:
logger.log_error(f"Currently not supporting multiple arenas for USD.")
env = self._env
results = env.import_from_usd_file(
cfg.fpath, return_object=True, cache_dir=self._convex_decomp_dir
)
# print("USD import results:", results)
articulations_found = []
for key, value in results.items():
if isinstance(value, dexsim.engine.Articulation):
articulations_found.append(value)
if len(articulations_found) == 0:
logger.log_error(f"No articulation found in USD file {cfg.fpath}.")
elif len(articulations_found) > 1:
logger.log_error(
f"Multiple articulations found in USD file {cfg.fpath}. "
)
elif len(articulations_found) == 1:
obj_list.append(articulations_found[0])
else:
# non-usd file does not support this option, will be forced set False to avoid potential issues.
cfg.use_usd_properties = False
for env in env_list:
art = env.load_urdf(cfg.fpath)
obj_list.append(art)
articulation = Articulation(cfg=cfg, entities=obj_list, device=self.device)
self._articulations[uid] = articulation
self.notify_visualization_topology_changed()
return articulation
[docs]
def get_articulation(self, uid: str) -> Articulation | None:
"""Get an articulation by its unique ID.
Args:
uid (str): The unique ID of the articulation.
Returns:
Articulation | None: The articulation instance if found, otherwise None.
"""
if uid not in self._articulations:
logger.log_warning(f"Articulation {uid} not found.")
return None
return self._articulations[uid]
[docs]
def get_articulation_uid_list(self) -> List[str]:
"""Get current articulation uid list
Returns:
List[str]: list of articulation uid.
"""
return list(self._articulations.keys())
[docs]
def add_robot(self, cfg: RobotCfg) -> Robot | None:
"""Add a Robot to the scene.
Args:
cfg (RobotCfg): Configuration for the robot.
Returns:
Robot | None: The added robot instance handle, or None if failed.
"""
uid = cfg.uid
if cfg.fpath is None:
if cfg.urdf_cfg is None:
logger.log_error(
"Robot configuration must have a valid fpath or urdf_cfg."
)
return None
cfg.fpath = cfg.urdf_cfg.assemble_urdf()
if cfg.solver_cfg is not None:
if isinstance(cfg.solver_cfg, dict):
for key, value in cfg.solver_cfg.items():
if hasattr(value, "urdf_path") and value.urdf_path is None:
value.urdf_path = cfg.fpath
if uid is None:
uid = os.path.splitext(os.path.basename(cfg.fpath))[0]
cfg.uid = uid
if uid in self._robots:
logger.log_error(f"Robot {uid} already exists.")
return self._robots[uid]
env_list = [self._env] if len(self._arenas) == 0 else self._arenas
obj_list = []
is_usd = cfg.fpath.endswith((".usd", ".usda", ".usdc"))
if is_usd:
# TODO: Currently add checking for num_envs when file is USD. After we support spawn via cloning, we can remove this.
if len(env_list) > 1:
logger.log_error(f"Currently not supporting multiple arenas for USD.")
env = self._env
results = env.import_from_usd_file(cfg.fpath, return_object=True)
# print("USD import results:", results)
articulations_found = []
for key, value in results.items():
if isinstance(value, dexsim.engine.Articulation):
articulations_found.append(value)
if len(articulations_found) == 0:
logger.log_error(f"No articulation found in USD file {cfg.fpath}.")
elif len(articulations_found) > 1:
logger.log_error(
f"Multiple articulations found in USD file {cfg.fpath}. "
)
elif len(articulations_found) == 1:
obj_list.append(articulations_found[0])
else:
# non-usd file does not support this option, will be forced set False to avoid potential issues.
cfg.use_usd_properties = False
for env in env_list:
art = env.load_urdf(cfg.fpath)
obj_list.append(art)
robot = Robot(cfg=cfg, entities=obj_list, device=self.device)
self._robots[uid] = robot
self.notify_visualization_topology_changed()
return robot
[docs]
def get_robot(self, uid: str) -> Robot | None:
"""Get a Robot by its unique ID.
Args:
uid (str): The unique ID of the robot.
Returns:
Robot | None: The robot instance if found, otherwise None.
"""
if uid not in self._robots:
logger.log_warning(f"Robot {uid} not found.")
return None
return self._robots[uid]
[docs]
def get_robot_uid_list(self) -> List[str]:
"""
Retrieves a list of unique identifiers (UIDs) for all robots in the V2 system.
Returns:
list: A list containing the UIDs of the robots.
"""
return list(self._robots.keys())
[docs]
def enable_entity_gizmo(
self,
config: EntityGizmoConfig | None = None,
) -> EntityGizmoManipulator:
"""Enable DexSim entity control and exclude the EmbodiChain ground.
Called automatically on the first native window unless disabled in
:class:`SimulationManagerCfg`. Explicit calls also work headlessly.
DexSim retains this controller and its configuration across window
close/reopen; reopening does not reapply the default configuration.
Args:
config: Native DexSim entity-Gizmo configuration.
Returns:
The active world-owned entity Gizmo manipulator.
"""
controller = (
self._world.enable_entity_gizmo()
if config is None
else self._world.enable_entity_gizmo(config)
)
self._auto_entity_gizmo_pending = False
default_plane = getattr(self, "_default_plane", None)
if default_plane is None:
return controller
result = controller.register_external_target(
self._DEFAULT_PLANE_GIZMO_TARGET_ID,
dexsim.interaction.EntityGizmoTargetType.RIGID_BODY,
default_plane,
ActorType.STATIC,
)
if result != dexsim.interaction.EntityGizmoResult.SUCCESS:
logger.log_warning(
"Failed to exclude the default plane from entity Gizmo "
f"control: {result}."
)
return controller
[docs]
def disable_entity_gizmo(self) -> None:
"""Disable native object interaction, including future window opens.
Call :meth:`enable_entity_gizmo` to explicitly re-enable interaction.
Robot IK controllers and Viser command permissions are independent.
"""
self._world.disable_entity_gizmo()
self._auto_entity_gizmo_pending = False
def _enable_default_entity_gizmo(self) -> None:
# Apply the startup default once; DexSim owns subsequent controller
# state, including explicit native disable calls and window reopens.
if self._window is not None and self._auto_entity_gizmo_pending:
self.enable_entity_gizmo()
[docs]
def enable_gizmo(
self,
uid: str,
control_part: str | None = None,
gizmo_cfg: GizmoCfg | None = None,
) -> Gizmo | None:
"""Register Gizmo control for a simulation target.
Robot controls support native windows and Viser; native IK activates
on the configured toggle key. Explicit configuration takes precedence
over automatically registered controls.
Args:
uid: UID of the robot, rigid object, or camera sensor.
control_part: Robot control part used for IK/FK.
gizmo_cfg: Gizmo appearance and robot IK configuration.
Returns:
The created Gizmo, or ``None`` if setup failed.
"""
# Create gizmo key combining uid and control_part
gizmo_key = f"{uid}:{control_part}" if control_part else uid
if hasattr(self, "_disabled_robot_gizmos"):
self._disabled_robot_gizmos.discard(gizmo_key)
# Check if gizmo already exists
if gizmo_key in self._gizmos:
if gizmo_cfg is not None:
self.disable_gizmo(uid, control_part)
if hasattr(self, "_disabled_robot_gizmos"):
self._disabled_robot_gizmos.discard(gizmo_key)
else:
return self._gizmos[gizmo_key]
# Search for target object in different collections
target = None
object_type = None
if uid in self._robots:
target = self._robots[uid]
object_type = "robot"
elif uid in self._rigid_objects:
target = self._rigid_objects[uid]
object_type = "rigid_object"
elif uid in self._sensors:
target = self._sensors[uid]
object_type = "sensor"
else:
logger.log_error(
f"Object with uid '{uid}' not found in any collection (robots, rigid_objects, sensors, articulations)."
)
return None
gizmo: Gizmo | None = None
try:
gizmo = Gizmo(target, gizmo_cfg, control_part)
self._gizmos[gizmo_key] = gizmo
self.notify_visualization_topology_changed()
logger.log_info(
f"Gizmo registered for {object_type} '{uid}' with "
f"control_part '{control_part}'."
)
except Exception as e:
if gizmo is not None:
gizmo.destroy()
logger.log_error(
f"Failed to create gizmo for {object_type} '{uid}' with control_part '{control_part}': {e}"
)
return None
return gizmo
[docs]
def disable_gizmo(self, uid: str, control_part: str | None = None) -> None:
"""Disable a Gizmo and prevent its automatic recreation.
Args:
uid: Target asset UID.
control_part: Robot control part. Omit to disable every part of a robot.
"""
gizmo_key = f"{uid}:{control_part}" if control_part else uid
if hasattr(self, "_disabled_robot_gizmos"):
self._disabled_robot_gizmos.add(gizmo_key)
robot = getattr(self, "_robots", {}).get(uid)
if robot is not None and control_part is None:
for key, gizmo in self.get_gizmo_items():
if key != gizmo_key and gizmo.target is robot:
self.disable_gizmo(key)
if gizmo_key not in self._gizmos:
if robot is not None:
return
logger.log_warning(
f"No gizmo found for '{uid}' with control_part '{control_part}'."
)
return
try:
gizmo = self._gizmos.pop(gizmo_key)
if self._picker_gizmo == (uid, control_part):
self._picker_gizmo = None
try:
if gizmo is not None:
gizmo.destroy()
finally:
self.notify_visualization_topology_changed()
logger.log_info(
f"Gizmo disabled for '{uid}' with control_part '{control_part}'"
)
except Exception as error:
logger.log_error(
f"Failed to disable gizmo for '{uid}' with control_part "
f"'{control_part}': {error}"
)
[docs]
def get_gizmo(
self,
uid: str,
control_part: str | None = None,
) -> Gizmo | None:
"""Return an active Gizmo.
Args:
uid: Target asset UID.
control_part: Robot control part, if applicable.
Returns:
Gizmo instance if found, otherwise ``None``.
"""
gizmo_key = f"{uid}:{control_part}" if control_part else uid
return self._gizmos.get(gizmo_key, None)
[docs]
def has_gizmo(self, uid: str, control_part: str | None = None) -> bool:
"""Check if a gizmo exists for the given UID and control part.
Args:
uid (str): Object UID to check
control_part (str | None, optional): Control part name for robots. Defaults to None.
Returns:
bool: True if gizmo exists, False otherwise.
"""
gizmo_key = f"{uid}:{control_part}" if control_part else uid
return gizmo_key in self._gizmos
[docs]
def list_gizmos(self) -> dict[str, bool]:
"""List active Gizmo IDs and availability.
Returns:
Mapping from ``uid[:control_part]`` to availability.
"""
return {
gizmo_key: (gizmo is not None) for gizmo_key, gizmo in self._gizmos.items()
}
[docs]
def get_gizmo_items(self) -> tuple[tuple[str, Gizmo], ...]:
"""Return a stable snapshot of active Gizmo IDs and controllers."""
return tuple(
(gizmo_key, gizmo)
for gizmo_key, gizmo in getattr(self, "_gizmos", {}).items()
if gizmo is not None
)
[docs]
def process_visualization_commands(self) -> int:
"""Apply queued Viser Gizmo commands on the simulation thread.
Returns:
Number of commands accepted for active Gizmos.
"""
runtime = self._visualization_runtime
if runtime is None or not getattr(
self.sim_config.visualization,
"allow_commands",
False,
):
return 0
accepted = 0
for command in runtime.drain_gizmo_commands():
if (
command.run_id != runtime.exporter.run_id
or command.scene_revision != runtime.exporter.scene_revision
):
continue
gizmo = self._gizmos.get(command.gizmo_id)
if gizmo is None:
continue
source_id = f"viser:{command.client_id}"
if command.phase in {"start", "update"} and not gizmo.begin_interaction(
source_id
):
continue
position = torch.as_tensor(
command.position,
dtype=torch.float32,
device=self.device,
)
position = position - self.arena_offsets[0]
wxyz = torch.as_tensor(
command.wxyz,
dtype=torch.float32,
device=self.device,
).unsqueeze(0)
pose = torch.eye(
4,
dtype=torch.float32,
device=self.device,
).unsqueeze(0)
pose[0, :3, :3] = matrix_from_quat(wxyz)[0]
pose[0, :3, 3] = position
if not gizmo.request_local_pose(pose, source_id=source_id):
continue
accepted += 1
if command.phase == "end":
gizmo.end_interaction(source_id)
return accepted
def _register_robot_gizmos(self) -> None:
"""Discover IK-capable parts without constructing another solver."""
cfg = getattr(self.sim_config, "robot_ik_gizmo", None)
if cfg is None or self.num_envs != 1:
return
native = self._window is not None
viser = (
self.sim_config.visualization.backend == "viser"
and self.sim_config.visualization.allow_commands
)
if not native and not viser:
return
for uid, robot in self._robots.items():
solver_cfgs = robot.cfg.solver_cfg
if not isinstance(solver_cfgs, dict):
continue
for part, solver_cfg in solver_cfgs.items():
key = f"{uid}:{part}"
if (
uid in self._disabled_robot_gizmos
or key in self._disabled_robot_gizmos
or key in self._gizmos
or part not in (robot.control_parts or {})
or not getattr(solver_cfg, "root_link_name", None)
or not getattr(solver_cfg, "end_link_name", None)
):
continue
if any(
gizmo.target is robot and gizmo.control_part == part
for _, gizmo in self.get_gizmo_items()
):
continue
try:
self.enable_gizmo(uid, part, deepcopy(cfg))
except Exception as error:
self._disabled_robot_gizmos.add(key)
logger.log_warning(
f"Could not register robot Gizmo '{key}': {error}"
)
[docs]
def update_gizmos(self) -> None:
"""Register robot controls, process interaction, and update active Gizmos."""
self._register_robot_gizmos()
self.process_pick_commands()
self.process_visualization_commands()
for gizmo_key, gizmo in list(
getattr(self, "_gizmos", {}).items()
): # Use list() to avoid modification during iteration
if gizmo is not None:
try:
if getattr(self, "_window", None) is not None:
gizmo.update_native(self._world)
gizmo.update()
except Exception as error:
logger.log_warning(f"Error updating gizmo '{gizmo_key}': {error}")
[docs]
def process_pick_commands(self) -> int:
"""Apply queued Viser click-pick commands on the simulation thread.
A non-empty pick attaches a picker-owned Gizmo to the clicked node;
an empty pick (clicking empty space) clears it. Only one picker-owned
Gizmo is kept at a time and user-created Gizmos are never touched.
Returns:
Number of pick commands drained from the visualization runtime.
"""
runtime = self._visualization_runtime
if runtime is None or not getattr(
self.sim_config.visualization, "allow_commands", False
):
return 0
processed = 0
for command in runtime.drain_pick_commands():
processed += 1
if (
command.run_id != runtime.exporter.run_id
or command.scene_revision != runtime.exporter.scene_revision
):
continue
if command.node_id is None:
# Clicking empty space detaches the picker-owned Gizmo.
self._release_picker_gizmo()
continue
resolved = runtime.exporter.resolve_node_target(command.node_id)
if resolved is None:
continue
uid, kind = resolved
if kind not in {"robot", "rigid"}:
logger.log_warning(
f"Pick target kind {kind!r} (uid {uid!r}) is not gizmo-able; "
"only rigid objects and robots can be picked."
)
self._release_picker_gizmo()
continue
# Re-clicking the already-picked target is a no-op (avoids flicker
# from recreating the same Gizmo, e.g. clicking near its handle).
if self._picker_gizmo is not None and self._picker_gizmo[0] == uid:
continue
self._release_picker_gizmo()
if any(key == uid or key.startswith(f"{uid}:") for key in self._gizmos):
continue
gizmo = self.enable_gizmo(uid=uid)
if gizmo is not None:
self._picker_gizmo = (uid, None)
return processed
def _release_picker_gizmo(self) -> None:
"""Detach the picker-owned Gizmo if one is currently attached."""
if self._picker_gizmo is None:
return
uid, control_part = self._picker_gizmo
self._picker_gizmo = None
if self.has_gizmo(uid, control_part=control_part):
self.disable_gizmo(uid, control_part=control_part)
[docs]
def toggle_gizmo_visibility(
self, uid: str, control_part: str | None = None
) -> bool | None:
"""Toggle Gizmo visibility and return the new state, if it exists."""
gizmo = self.get_gizmo(uid, control_part)
if gizmo is not None:
return gizmo.toggle_visibility()
return None
[docs]
def set_gizmo_visibility(
self, uid: str, visible: bool, control_part: str | None = None
) -> None:
"""Set Gizmo visibility by target UID and optional control part."""
gizmo = self.get_gizmo(uid, control_part)
if gizmo is not None:
gizmo.set_visible(visible)
[docs]
def add_sensor(self, sensor_cfg: SensorCfg) -> BaseSensor:
"""General interface to add a sensor to the scene and returns a handle.
Args:
sensor_cfg (SensorCfg): configuration for the sensor.
Returns:
BaseSensor: The added sensor instance handle.
"""
sensor_type = sensor_cfg.sensor_type
if sensor_type not in self.SUPPORTED_SENSOR_TYPES:
logger.log_warning(f"Unsupported sensor type: {sensor_type}")
return None
sensor_uid = sensor_cfg.uid
if sensor_uid is None:
sensor_uid = f"{sensor_type.lower()}_{len(self._sensors)}"
sensor_cfg.uid = sensor_uid
if sensor_uid in self._sensors:
logger.log_warning(f"Sensor {sensor_uid} already exists.")
return None
parent_nodes = None
if (
isinstance(sensor_cfg, CameraCfg)
and sensor_cfg.extrinsics.parent is not None
):
parent_nodes = resolve_parent_nodes(
parent=sensor_cfg.extrinsics.parent,
assets={**self._articulations, **self._robots},
num_envs=self.num_envs,
)
sensor = self.SUPPORTED_SENSOR_TYPES[sensor_type](sensor_cfg, self.device)
if isinstance(sensor, Camera) and parent_nodes is not None:
sensor.attach_to_parent_nodes(parent_nodes)
self._sensors[sensor_uid] = sensor
if isinstance(sensor, Camera):
self.notify_visualization_topology_changed()
return sensor
[docs]
def get_sensor(self, uid: str) -> BaseSensor | None:
"""Get a sensor by its UID.
Args:
uid (str): The UID of the sensor.
Returns:
BaseSensor | None: The sensor instance if found, otherwise None.
"""
if uid not in self._sensors:
logger.log_warning(f"Sensor {uid} not found.")
return None
return self._sensors[uid]
[docs]
def get_sensor_uid_list(self) -> List[str]:
"""Get current sensor uid list
Returns:
List[str]: list of sensor uid.
"""
return list(self._sensors.keys())
[docs]
def remove_asset(self, uid: str) -> bool:
"""Remove an asset by its UID.
The asset can be a light, sensor, robot, rigid object or articulation.
Note:
Currently, lights and sensors are not supported to be removed.
Args:
uid (str): The UID of the asset.
Returns:
bool: True if the asset is removed successfully, otherwise False.
"""
if uid in self._rigid_objects:
obj = self._rigid_objects.pop(uid)
obj.destroy()
self.notify_visualization_topology_changed()
return True
if uid in self._soft_objects:
obj = self._soft_objects.pop(uid)
obj.destroy()
self.notify_visualization_topology_changed()
return True
if uid in self._cloth_objects:
obj = self._cloth_objects.pop(uid)
obj.destroy()
self.notify_visualization_topology_changed()
return True
if uid in self._rigid_object_groups:
group = self._rigid_object_groups.pop(uid)
group.destroy()
self.notify_visualization_topology_changed()
return True
if uid in self._articulations:
art = self._articulations.pop(uid)
art.destroy()
self.notify_visualization_topology_changed()
return True
if uid in self._robots:
robot = self._robots.pop(uid)
for key, gizmo in self.get_gizmo_items():
if gizmo.target is robot:
self.disable_gizmo(key)
self._disabled_robot_gizmos = {
key
for key in self._disabled_robot_gizmos
if key != uid and not key.startswith(f"{uid}:")
}
robot.destroy()
self.notify_visualization_topology_changed()
return True
return False
[docs]
def draw_marker(
self,
cfg: MarkerCfg,
) -> MeshObject:
"""Draw visual markers in the simulation scene for debugging and visualization.
Drawing markers does not advance physics. Call
:meth:`capture_visualization` to publish them to Viser without stepping.
Args:
cfg (MarkerCfg): Marker configuration with the following key parameters:
- name (str): Unique identifier for the marker group
- marker_type (str): Type of marker ("axis" currently supported)
- axis_xpos (np.ndarray | List[np.ndarray]): 4x4 transformation matrices
for marker positions and orientations
- axis_size (float): Thickness of axis arrows
- axis_len (float): Length of axis arrows
- arena_index (int): Arena index for placement (-1 for global)
Returns:
List[MeshObject]: List of created marker handles, False if invalid input,
None if no poses provided.
Example:
```python
cfg = MarkerCfg(name="test_axis", marker_type="axis", axis_xpos=np.eye(4))
markers = sim.draw_marker(cfg)
```
"""
# Validate marker type
if cfg.marker_type != "axis":
logger.log_error(
f"Unsupported marker type '{cfg.marker_type}'. Currently only 'axis' is supported."
)
return False
draw_xpos = deepcopy(cfg.axis_xpos)
if isinstance(draw_xpos, torch.Tensor):
draw_xpos = draw_xpos.detach().cpu().numpy()
elif isinstance(draw_xpos, (list, tuple)):
draw_xpos = [
item.detach().cpu().numpy() if isinstance(item, torch.Tensor) else item
for item in draw_xpos
]
draw_xpos = np.array(draw_xpos)
if draw_xpos.ndim == 2:
if draw_xpos.shape == (4, 4):
draw_xpos = np.expand_dims(draw_xpos, axis=0)
else:
logger.log_error(
f"axis_xpos must be of shape (N, 4, 4), got {draw_xpos.shape}."
)
return False
elif draw_xpos.ndim != 3 or draw_xpos.shape[1:] != (4, 4):
logger.log_error(
f"axis_xpos must be of shape (N, 4, 4), got {draw_xpos.shape}."
)
return False
original_name = cfg.name
name = original_name
count = 0
while name in self._markers:
count += 1
name = f"{original_name}_{count}"
if count > 0:
logger.log_warning(
f"Marker name '{original_name}' already exists. Using '{name}'."
)
marker_num = len(draw_xpos)
if marker_num == 0:
logger.log_warning(f"No marker poses provided.")
return None
if cfg.arena_index >= 0:
name = f"{name}_{cfg.arena_index}"
env = self.get_env(cfg.arena_index)
# Create markers based on marker type
marker_handles = []
if cfg.marker_type == "axis":
# Create coordinate axes
axis_option = dexsim.types.AxisOption(
lx=cfg.axis_len,
ly=cfg.axis_len,
lz=cfg.axis_len,
size=cfg.axis_size,
arrow_type=cfg.arrow_type,
corner_type=cfg.corner_type,
tag_type=dexsim.types.AxisTagType.NONE,
)
for i, pose in enumerate(draw_xpos):
axis_handle = env.create_axis(axis_option)
axis_handle.set_local_pose(pose)
marker_handles.append(axis_handle)
# TODO: Add support for other marker types in the future
# elif cfg.marker_type == "line":
# # Create line markers
# pass
# elif cfg.marker_type == "point":
# # Create point markers
# pass
self._markers[name] = _AxisMarkerGroup(
handles=tuple(marker_handles),
arena_index=cfg.arena_index,
axis_length=cfg.axis_len,
axis_radius=cfg.axis_size,
)
return marker_handles
[docs]
def remove_marker(self, name: str) -> bool:
"""Remove markers (including axis) with the given name.
Args:
name (str): The name of the marker to remove.
Returns:
bool: True if the marker was removed successfully, False otherwise.
"""
if name not in self._markers:
logger.log_warning(f"Marker {name} not found.")
return False
try:
marker_group = self._markers[name]
env = self.get_env(marker_group.arena_index)
for marker_handle in marker_group.handles:
if marker_handle is not None:
env.remove_actor(marker_handle.get_name())
self._markers.pop(name)
return True
except Exception as e:
logger.log_warning(f"Failed to remove marker {name}: {str(e)}")
return False
[docs]
def get_axis_marker_items(
self,
) -> tuple[tuple[str, tuple[MeshObject, ...], float, float], ...]:
"""Return active axes for backend-neutral visualization.
Returns:
Tuples containing the marker name, native handles, axis length, and
axis radius for each active marker group.
"""
return tuple(
(
name,
group.handles,
group.axis_length,
group.axis_radius,
)
for name, group in self._markers.items()
)
[docs]
def add_custom_window_control(self, controls: list[ObjectManipulator]) -> None:
"""Add one or more custom window input controls.
This method registers additional :class:`ObjectManipulator` instances
with the simulation window so they can handle input events alongside
any default controls.
Args:
controls (list[ObjectManipulator]): A list of initialized
ObjectManipulator instances to add to the current window.
Each control will be registered via ``window.add_input_control``.
If no window is available, the controls are not added and a
warning is logged.
"""
if self._window is None:
logger.log_warning("No window available to add custom controls.")
return
for control in controls:
self._window.add_input_control(control)
def _build_window_record_output(
self, save_path: str | None, video_prefix: str
) -> tuple[str, str]:
"""Resolve the output directory and file name for viewer recording."""
if save_path is None:
output_dir = os.path.join(os.getcwd(), "outputs", "videos")
timestamp = datetime.now().strftime("%Y-%m-%d-%H-%M-%S")
video_name = f"{video_prefix}_{timestamp}"
else:
output_dir = os.path.dirname(save_path) or os.getcwd()
video_name = Path(os.path.basename(save_path)).stem
return output_dir, video_name
[docs]
def is_window_recording(self) -> bool:
"""Check whether the viewer window is currently recording."""
return self._window_record_state is not None
def _build_window_record_pose_from_look_at(
self,
eye: Sequence[float],
target: Sequence[float],
up: Sequence[float] = (0.0, 0.0, 1.0),
) -> np.ndarray:
"""Build a camera pose matrix for the recorder from look-at inputs."""
pose = look_at_to_pose(eye, target, up)[0].cpu().numpy()
pose[:3, 1] = -pose[:3, 1]
pose[:3, 2] = -pose[:3, 2]
return np.asarray(pose, dtype=np.float32)
def _resolve_window_record_pose(
self, state: _WindowRecordState
) -> np.ndarray | None:
"""Resolve the camera pose used by the recorder for the current frame."""
if state.pose_provider is not None:
pose = state.pose_provider()
return np.asarray(pose, dtype=np.float32)
if state.fixed_pose is not None:
return np.asarray(state.fixed_pose, dtype=np.float32)
if self._window is not None:
return np.asarray(self._window.get_pose_matrix(), dtype=np.float32)
return None
def _step_window_record(self, state: _WindowRecordState) -> int:
"""Capture frames in the render thread without blocking the UI loop."""
if state.task_status != TASK_RETURN.TASK_LOOP:
return state.task_status
now = time.time()
if now - state.last_capture_time < state.time_step:
return state.task_status
state.last_capture_time = now
return self._capture_window_record_frame(state)
def _capture_window_record_frame(self, state: _WindowRecordState) -> int:
"""Render one frame for the active recording session."""
frame: np.ndarray | None = None
pose = self._resolve_window_record_pose(state)
if pose is not None and state.record_camera is not None:
state.record_camera.set_world_pose(pose)
state.record_camera.render()
rgb = np.asarray(state.record_camera.get_rgb_map())
if rgb.size != 0:
frame = np.ascontiguousarray(rgb[..., :3])
if frame is None:
return state.task_status
state.frames.append(frame)
state.current_memory_bytes += frame.nbytes
if state.current_memory_bytes > state.max_memory_bytes:
logger.log_warning(
"Viewer recording exceeded the configured memory budget. "
"Press 'r' again to flush the buffered frames to disk."
)
state.task_status = TASK_RETURN.TASK_EXIT
return state.task_status
def _step_window_record_from_sim_update(
self, state: _WindowRecordState, physics_dt: float
) -> int:
"""Capture recording frames based on simulation time progression."""
if state.task_status != TASK_RETURN.TASK_LOOP:
return state.task_status
state.accumulated_sim_time += physics_dt
if state.accumulated_sim_time + 1e-9 < state.time_step:
return state.task_status
state.accumulated_sim_time = max(
0.0, state.accumulated_sim_time - state.time_step
)
return self._capture_window_record_frame(state)
def _save_window_record_worker(
self,
frames: list[np.ndarray],
output_dir: str,
video_name: str,
save_kwargs: dict[str, object],
) -> None:
"""Encode buffered frames into a video file in a background thread."""
from dexsim.utility import images_to_video
try:
os.makedirs(output_dir, exist_ok=True)
images_to_video(
images=frames,
output_dir=output_dir,
video_name=video_name,
**save_kwargs,
)
logger.log_info(
f"Viewer recording saved to {os.path.join(output_dir, video_name + '.mp4')}"
)
except Exception as exc:
logger.log_error(f"Failed to save viewer recording: {exc}")
[docs]
def start_window_record(
self,
save_path: str | None = None,
fps: int = 20,
max_memory: int = 1024,
video_prefix: str = "viewer_record",
pose_provider: Callable[[], np.ndarray] | None = None,
fixed_pose: np.ndarray | None = None,
look_at: (
tuple[
Sequence[float],
Sequence[float],
Sequence[float],
]
| None
) = None,
use_sim_time: bool | None = None,
) -> bool:
"""Start asynchronously recording the simulation to a video buffer.
The recorder can either follow the live viewer camera or run without a
window by using a fixed pose or a pose callback supplied by the caller.
Args:
save_path: Optional output path for the recorded video.
fps: Target output frames per second. Must be positive.
max_memory: Maximum buffered frame memory in MB. Must be positive.
video_prefix: File name prefix used when ``save_path`` is not provided.
pose_provider: Optional callback that returns the current camera pose.
fixed_pose: Optional fixed 4x4 camera pose matrix.
look_at: Optional ``(eye, target, up)`` tuple used to derive a fixed pose.
use_sim_time: Whether to capture frames from simulation time instead of
wall time. Defaults to headless mode when no viewer window exists.
Returns:
bool: True if recording starts successfully, otherwise False.
"""
if self.is_window_recording():
logger.log_error(
"A viewer recording session is already active. Stop it before starting a new recording."
)
if fps <= 0:
logger.log_error(f"Viewer recording FPS must be positive, got {fps}.")
if max_memory <= 0:
logger.log_error(
f"Viewer recording max_memory must be positive, got {max_memory}."
)
if pose_provider is not None and fixed_pose is not None:
logger.log_error(
"Recorder accepts only one explicit pose source: `pose_provider` or `fixed_pose`."
)
if pose_provider is not None and look_at is not None:
logger.log_error(
"Recorder accepts only one explicit pose source: `pose_provider` or `look_at`."
)
if fixed_pose is not None and look_at is not None:
logger.log_error(
"Recorder accepts only one explicit pose source: `fixed_pose` or `look_at`."
)
if look_at is not None:
fixed_pose = self._build_window_record_pose_from_look_at(*look_at)
if pose_provider is None and fixed_pose is None and self._window is None:
logger.log_warning(
"No simulation window available for viewer recording. "
"Provide `pose_provider`, `fixed_pose`, or `look_at` to record in headless mode."
)
return False
if use_sim_time is None:
use_sim_time = self._window is None
width = self.sim_config.width
height = self.sim_config.height
if self._window_record_camera is None:
camera_name = f"viewer_record_camera_{self.instance_id}"
self._window_record_camera = self._env.create_camera(
camera_name, width, height
)
record_camera = self._window_record_camera
if hasattr(record_camera, "is_open") and record_camera.is_open() is False:
record_camera.open_camera()
time_step = 1.0 / float(fps)
output_dir, video_name = self._build_window_record_output(
save_path, video_prefix
)
state = _WindowRecordState(
time_step=time_step,
max_memory_bytes=max_memory * 1024 * 1024,
output_dir=output_dir,
video_name=video_name,
save_kwargs={"fps": fps},
record_camera=record_camera,
pose_provider=pose_provider,
fixed_pose=(
None if fixed_pose is None else np.asarray(fixed_pose, dtype=np.float32)
),
capture_from_sim_update=use_sim_time,
last_capture_time=time.time() - time_step,
)
if not state.capture_from_sim_update:
def _window_record_loop(_: float) -> int:
return self._step_window_record(state)
state.loop_handle = self._world.thread_rt().add_loop(
_window_record_loop, time_step
)
self._window_record_state = state
follow_source = (
"live viewer pose"
if pose_provider is None and fixed_pose is None and self._window is not None
else "custom pose source"
)
timing_source = (
"simulation time" if state.capture_from_sim_update else "wall time"
)
save_target = os.path.join(output_dir, video_name + ".mp4")
if self._window is not None:
logger.log_info(
f"Viewer recording started ({follow_source}, {timing_source}). Press 'r' again to stop and save to "
f"{save_target}"
)
else:
logger.log_info(
f"Viewer recording started ({follow_source}, {timing_source}). Call `stop_window_record()` to save to "
f"{save_target}"
)
return True
[docs]
def stop_window_record(self, save_path: str | None = None) -> bool:
"""Stop the active viewer recording and save frames in the background."""
if self._window_record_state is None:
logger.log_warning("No active viewer recording session found.")
return False
state = self._window_record_state
state.task_status = TASK_RETURN.TASK_EXIT
if save_path is not None:
output_dir, video_name = self._build_window_record_output(
save_path, "viewer_record"
)
else:
output_dir, video_name = state.output_dir, state.video_name
if state.record_camera is not None and hasattr(state.record_camera, "is_open"):
if state.record_camera.is_open():
state.record_camera.close_camera()
frames = list(state.frames)
self._window_record_state = None
if len(frames) == 0:
logger.log_warning(
"Viewer recording stopped, but no frames were captured. Skipping video export."
)
return False
self._window_record_save_threads = [
thread for thread in self._window_record_save_threads if thread.is_alive()
]
save_thread = threading.Thread(
target=self._save_window_record_worker,
args=(frames, output_dir, video_name, dict(state.save_kwargs)),
daemon=False,
)
save_thread.start()
self._window_record_save_threads.append(save_thread)
logger.log_info(
"Viewer recording stopped. Saving video to "
f"{os.path.join(output_dir, video_name + '.mp4')} in background."
)
return True
[docs]
def wait_window_record_saves(self) -> None:
"""Wait for all background video export threads to finish."""
for thread in self._window_record_save_threads:
thread.join()
self._window_record_save_threads = []
[docs]
def toggle_window_record(
self,
save_path: str | None = None,
fps: int = 20,
max_memory: int = 1024,
video_prefix: str = "viewer_record",
) -> bool:
"""Toggle viewer recording on or off."""
if self.is_window_recording():
return self.stop_window_record(save_path=save_path)
return self.start_window_record(
save_path=save_path,
fps=fps,
max_memory=max_memory,
video_prefix=video_prefix,
)
[docs]
def enable_window_record_hotkey(
self,
save_path: str | None = None,
fps: int = 20,
max_memory: int = 1024,
video_prefix: str = "viewer_record",
) -> bool:
"""Register the ``r`` key to start/stop viewer recording."""
self._window_record_hotkey_cfg = {
"save_path": save_path,
"fps": fps,
"max_memory": max_memory,
"video_prefix": video_prefix,
}
if self._window is None:
logger.log_warning(
"No simulation window available yet. The viewer record hotkey will be registered after `open_window()`."
)
return False
if self._window_record_input_control is not None:
return True
from dexsim.types import InputKey
sim = self
hotkey_cfg = dict(self._window_record_hotkey_cfg)
class WindowRecordEvent(ObjectManipulator):
def on_key_down(self, key):
if key == InputKey.SCANCODE_R.value:
sim.toggle_window_record(**hotkey_cfg)
self._window_record_input_control = WindowRecordEvent()
self._window.add_input_control(self._window_record_input_control)
logger.log_info(
"Viewer record hotkey registered. Press 'r' to start/stop recording."
)
return True
@staticmethod
def _window_camera_pose_to_look_at(
pose: np.ndarray,
) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
"""Convert a DexSim window model matrix to look-at vectors.
DexSim stores the viewer camera model matrix with columns
``[right, up, -forward]``. The local camera up axis changes while the
viewer orbits, but ``Windows.set_look_at`` uses a world-up reference.
Always use DexSim's default Z-up vector so a captured snippet retains
the standard viewer controls.
Args:
pose: A 4x4 homogeneous viewer camera pose matrix.
Returns:
The ``(eye, look_at, up)`` vectors accepted by
``Windows.set_look_at``.
Raises:
ValueError: If ``pose`` is not a 4x4 homogeneous matrix.
"""
matrix = np.asarray(pose, dtype=np.float64)
if matrix.shape != (4, 4):
raise ValueError(
f"Window camera pose must have shape (4, 4), got {matrix.shape}."
)
eye = matrix[:3, 3]
look_at = eye - matrix[:3, 2]
up = np.array([0.0, 0.0, 1.0], dtype=np.float64)
return eye, look_at, up
@staticmethod
def _format_window_camera_pose(
pose: np.ndarray, convert_to_look_at: bool = True
) -> str:
"""Format a DexSim window pose as an executable Python snippet.
Args:
pose: A 4x4 homogeneous viewer camera pose matrix.
convert_to_look_at: Print a ``set_look_at`` call when true;
otherwise print the raw pose matrix.
Returns:
An executable Python snippet containing the camera pose.
Raises:
ValueError: If ``pose`` is not a 4x4 homogeneous matrix.
"""
matrix = np.asarray(pose, dtype=np.float64)
if matrix.shape != (4, 4):
raise ValueError(
f"Window camera pose must have shape (4, 4), got {matrix.shape}."
)
def _format_float(value: float) -> str:
if abs(value) < 1e-12:
return "0.0"
formatted = format(value, ".8g")
if "e" not in formatted and "." not in formatted:
formatted += ".0"
return formatted
def _vector_literal(vector: np.ndarray) -> str:
values = ", ".join(_format_float(float(value)) for value in vector)
return f"np.array([{values}], dtype=np.float32)"
if convert_to_look_at:
eye, look_at, up = SimulationManager._window_camera_pose_to_look_at(matrix)
return (
"window.set_look_at("
f"eye={_vector_literal(eye)}, "
f"look_at={_vector_literal(look_at)}, "
f"up={_vector_literal(up)})"
)
rows = ",\n ".join(
"[" + ", ".join(_format_float(float(value)) for value in row) + "]"
for row in matrix
)
return f"window_pose = np.array([\n {rows}\n], dtype=np.float32)"
[docs]
def print_window_camera_pose(self, convert_to_look_at: bool = True) -> str | None:
"""Print the current viewer camera pose as reusable Python code.
Args:
convert_to_look_at: Print ``window.set_look_at(...)`` by default.
Set false to print the raw 4x4 pose matrix instead.
Returns:
The printed snippet, or ``None`` when no viewer window is open.
"""
if self._window is None:
logger.log_warning("No simulation window available to print its pose.")
return None
pose = np.asarray(self._window.get_pose_matrix(), dtype=np.float32)
snippet = self._format_window_camera_pose(pose, convert_to_look_at)
print(snippet)
return snippet
[docs]
def enable_window_camera_pose_hotkey(self, convert_to_look_at: bool = True) -> bool:
"""Register ``p`` to print the current viewer camera pose.
Args:
convert_to_look_at: Print a ``window.set_look_at(...)`` call when
true, which is the default. Set false to print the raw matrix.
Returns:
Whether the control is registered on an available window.
"""
self._window_camera_pose_hotkey_cfg = {"convert_to_look_at": convert_to_look_at}
if self._window is None:
logger.log_warning(
"No simulation window available yet. The camera pose print "
"hotkey will be registered after `open_window()`."
)
return False
if self._window_camera_pose_input_control is not None:
return True
from dexsim.types import InputKey
sim = self
hotkey_cfg = dict(self._window_camera_pose_hotkey_cfg)
class WindowCameraPoseEvent(ObjectManipulator):
def on_key_down(self, key):
if key == InputKey.SCANCODE_P.value:
sim.print_window_camera_pose(**hotkey_cfg)
self._window_camera_pose_input_control = WindowCameraPoseEvent()
self._window.add_input_control(self._window_camera_pose_input_control)
logger.log_info(
"Camera pose print hotkey registered. Press 'p' to print the "
"current viewer pose."
)
return True
[docs]
def create_visual_material(self, cfg: VisualMaterialCfg) -> VisualMaterial:
"""Create a visual material with given configuration.
Args:
cfg (VisualMaterialCfg): configuration for the visual material.
Returns:
VisualMaterial: the created visual material instance handle.
"""
if cfg.uid in self._visual_materials:
logger.log_warning(
f"Visual material {cfg.uid} already exists. Returning the existing one."
)
return self._visual_materials[cfg.uid]
mat: Material = self._env.create_pbr_material(cfg.uid, True)
visual_mat = VisualMaterial(cfg, mat)
self._visual_materials[cfg.uid] = visual_mat
return visual_mat
[docs]
def get_visual_material(self, uid: str) -> VisualMaterial:
"""Get visual material by UID.
Args:
uid (str): uid of visual material.
"""
if uid not in self._visual_materials:
logger.log_warning(f"Visual material {uid} not found.")
return None
return self._visual_materials[uid]
[docs]
def clean_materials(self):
self._visual_materials = {}
if self._env:
self._env.clean_materials()
[docs]
def reset_objects_state(
self,
env_ids: Sequence[int] | None = None,
excluded_uids: Sequence[str] | None = None,
) -> None:
"""Reset the state of the simulated assets given the environment IDs and excluded UIDs.
Args:
env_ids (Sequence[int] | None): The environment IDs to reset. If None, reset all environments.
excluded_uids (Sequence[str] | None): List of asset UIDs to exclude from resetting. If None, reset all assets.
"""
excluded_uids = set(excluded_uids) if excluded_uids is not None else set()
for uid, robot in self._robots.items():
if uid not in excluded_uids:
robot.reset(env_ids)
for uid, articulation in self._articulations.items():
if uid not in excluded_uids:
articulation.reset(env_ids)
for uid, rigid_obj in self._rigid_objects.items():
if uid not in excluded_uids:
rigid_obj.reset(env_ids)
for uid, rigid_obj_group in self._rigid_object_groups.items():
if uid not in excluded_uids:
rigid_obj_group.reset(env_ids)
for uid, soft_obj in self._soft_objects.items():
if uid not in excluded_uids:
soft_obj.reset(env_ids)
for uid, cloth_obj in self._cloth_objects.items():
if uid not in excluded_uids:
cloth_obj.reset(env_ids)
for uid, light in self._lights.items():
if uid not in excluded_uids:
light.reset(env_ids)
for uid, sensor in self._sensors.items():
if uid not in excluded_uids:
sensor.reset(env_ids)
[docs]
def export_usd(self, fpath: str) -> bool:
"""Export the current simulation scene to a USD file.
Args:
fpath (str): The file path to save the USD file.
Returns:
bool: True if export is successful, False otherwise.
"""
try:
self._env.export_to_usd_file(fpath)
logger.log_info(f"Simulation scene exported to USD file: {fpath}")
return True
except Exception as e:
logger.log_error(f"Failed to export simulation scene to USD: {e}")
return False
[docs]
@staticmethod
def wait_scene_destruction(timeout_ms: int = 10000) -> None:
"""A public helper to wait for the underlying C++ scenes (dexsim.World) to destruct completely."""
import dexsim
import gc
# Force garbage collection to break cycle references
gc.collect()
import time
wait_times = 0
scene_count = dexsim.get_world_num()
max_loops = timeout_ms // 10
while scene_count > 0 and wait_times < max_loops:
time.sleep(0.01)
scene_count = dexsim.get_world_num()
wait_times += 1
if wait_times % 50 == 0:
from embodichain.utils import logger
logger.log_info(
f"Waiting for dexsim.World scenes to destruct. Remaining scenes: {scene_count}"
)
if scene_count > 0:
from embodichain.utils import logger
logger.log_warning(
f"Scene destruction wait timeout, {scene_count} C++ scene(s) still alive!"
)
[docs]
def destroy(self, exit_process: bool | None = None) -> None:
"""
No longer destructs C++ objects in place due to lingering deep local variables;
instead, packages itself into a destruction task, submits to the cleanup queue,
and waits for top-level delayed consumption.
Args:
exit_process (bool | None): Whether to call os._exit(0) after queuing
the destruction task. If None, reads EMBODICHAIN_SIM_EXIT_PROCESS.
"""
try:
self.stop_visualization()
except Exception as error:
logger.log_warning(f"Failed to stop Viser visualization cleanly: {error!r}")
if exit_process is None:
exit_process = (
os.getenv("EMBODICHAIN_SIM_EXIT_PROCESS", "1").strip().lower()
)
exit_process = exit_process not in ("0", "false", "no", "off")
self._is_pending_kill = True
# Transfer the actual destruction logic to the cleanup queue
SimulationManager._cleanup_queue.put(self._deferred_destroy)
if exit_process:
os._exit(0)
def _deferred_destroy(self) -> None:
"""Destroy all simulated assets and release resources."""
# Clean up all gizmos before destroying the simulation
for uid in list(self._gizmos.keys()):
self.disable_gizmo(uid)
if self.is_window_recording():
self.stop_window_record()
self.wait_window_record_saves()
# Stop the render loop before releasing scene resources. Vulkan window
# presentation may otherwise continue acquiring swapchain images while
# Env::Clean tears down render objects used by the in-flight frame.
if getattr(self, "is_window_opened", False):
self.close_window()
import sys, gc
self.clean_materials()
if self._env:
self._env.clean()
if self._world:
self._world.quit()
# REMOVE INSTANCE FROM POOL
instance_id = getattr(self, "instance_id", 0)
SimulationManager.reset(instance_id)
# Helper to aggressively decouple C++ wrapped objects
def _sever_wrapper_refs(obj_registry):
if not hasattr(self, obj_registry):
return
registry = getattr(self, obj_registry)
if not isinstance(registry, dict):
return
for uid, obj in registry.items():
if hasattr(obj, "_world"):
obj._world = None
if hasattr(obj, "_ps"):
obj._ps = None
if hasattr(obj, "_env"):
obj._env = None
if hasattr(obj, "_entities"):
obj._entities = []
registry.clear()
_sever_wrapper_refs("_gizmos")
_sever_wrapper_refs("_markers")
_sever_wrapper_refs("_rigid_objects")
_sever_wrapper_refs("_constraints")
_sever_wrapper_refs("_rigid_object_groups")
_sever_wrapper_refs("_soft_objects")
_sever_wrapper_refs("_cloth_objects")
_sever_wrapper_refs("_articulations")
_sever_wrapper_refs("_robots")
_sever_wrapper_refs("_sensors")
_sever_wrapper_refs("_lights")
# Explicitly clear Python references to trigger C++ object destructors
self._ps = None
self._env = None
self._world = None
self._default_plane = None
# Try to break ANY possible frame cycle
gc.collect()
self._visual_materials.clear()
self._texture_cache.clear()
self._arenas.clear()
self._markers.clear()
self._gizmos.clear()
self._constraints.clear()
SimulationManager.reset(self.instance_id)
# Forcefully drop underlying C++ object wrappers
self._env = None
self._world = None
gc.collect()
[docs]
@staticmethod
def flush_cleanup_queue() -> None:
"""Run pending destruction tasks and wait for their scenes to disappear.
An empty queue means that no manager requested destruction. In that
case, returning immediately is important: other managers may still own
live worlds, and waiting for the global world count to reach zero would
block until the timeout even though there is nothing to clean up.
"""
import gc
drained_task = False
while True:
try:
task = SimulationManager._cleanup_queue.get_nowait()
except queue.Empty:
break
drained_task = True
try:
task()
except Exception as e:
from embodichain.utils import logger
logger.log_error(f"Error during delayed destruction: {e}")
if not drained_task:
return
# After the queue is emptied, perform a top-level full GC to thoroughly reclaim dead objects that haven't released their RefPtrs yet
gc.collect()
# At this point, wait for the C++ Scene to return to zero, since the stack is at the top level, there will definitely be no deadlock
SimulationManager.wait_scene_destruction()