# ----------------------------------------------------------------------------
# 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
from collections.abc import Mapping
from math import log
from functools import wraps
from datetime import datetime
import os
import threading
import torch
import numpy as np
import gymnasium as gym
from dataclasses import MISSING
from typing import (
TYPE_CHECKING,
Dict,
Union,
Sequence,
Tuple,
Any,
Iterable,
List,
Optional,
)
from tensordict import TensorDict
from embodichain.lab.sim.cfg import (
RobotCfg,
RigidObjectCfg,
RigidObjectGroupCfg,
ArticulationCfg,
LightCfg,
)
from embodichain.lab.gym.envs.action_bank.configurable_action import (
get_func_tag,
)
from embodichain.lab.gym.envs.action_bank.configurable_action import (
ActionBank,
)
from embodichain.lab.sim.objects import Robot
from embodichain.lab.sim.sensors import BaseSensor, SensorCfg
from embodichain.lab.sim.types import EnvObs, EnvAction
from embodichain.lab.gym.envs import BaseEnv, EnvCfg
from embodichain.lab.gym.envs.demo import (
DEMO_SCHEMA_VERSION,
DemoExecutionCfg,
DemoEpisodeResult,
DemoSegment,
DemoSegmentResult,
)
from embodichain.lab.gym.envs.types import ControllerAction
from embodichain.lab.gym.envs.managers import (
EventManager,
ObservationManager,
RewardManager,
ActionManager,
DatasetManager,
)
from embodichain.lab.gym.utils.registration import register_env
from embodichain.lab.gym.utils.gym_utils import (
build_trajectory_buffer,
init_rollout_buffer_from_gym_space,
)
from embodichain.lab.gym.utils.trajectory_state import capture_trajectory_state
from embodichain.utils import configclass, logger
from embodichain.data import get_data_path
from embodichain.data.constants import EMBODICHAIN_DEFAULT_DATA_ROOT
if TYPE_CHECKING:
from embodichain.lab.task_program import CompiledTaskProgram, TaskProgramCfg
from embodichain.lab.task_program.integrations import (
TaskProgramAdapterFactory,
TaskProgramEnvironmentAdapter,
)
from embodichain.lab.gym.envs.task_program.bridge import TaskProgramDemoBridge
__all__ = ["EmbodiedEnvCfg", "EmbodiedEnv"]
[docs]
@configclass
class EmbodiedEnvCfg(EnvCfg):
"""Configuration for Embodied AI environments.
`EmbodiedEnvCfg` extends `EnvCfg` with high-level scene, robot, sensor,
object and manager declarations used to build modular embodied environments.
The configuration is intended to be declarative: the environment and its
managers (events, observations, rewards, dataset) are assembled from the
provided config fields with minimal additional code.
Typical usage: declare robots, sensors, lights, rigid objects/articulations,
and manager configurations. Additional task-specific parameters can be
supplied via the `extensions` dict and will be bound to the environment
instance as attributes during initialization.
Key fields
- **robot**: `RobotCfg` (required) — the agent definition (URDF/MJCF, initial
state, control mode, etc.).
- **control_parts**: Optional[List[str]] — named robot parts to control. If
`None`, all controllable joints are used.
- **active_joint_ids**: List[int] — explicit joint indices to use for
control (alternative to `control_parts`).
- **sensor**: List[`SensorCfg`] — sensors attached to the robot or scene
(cameras, depth, segmentation, force sensors, ...).
- **light**: `EnvLightCfg` — lighting configuration (direct lights now,
indirect/IBL planned for future releases).
- **background**, **rigid_object**, **rigid_object_group**, **articulation**:
scene object lists for static/kinematic props, dynamic objects, grouped
object pools, and articulated mechanisms respectively.
- **events**: Optional manager config — event functors for startup/reset/
periodic randomization and scripted behaviors.
- **observations**, **rewards**, **dataset**: Optional manager configs to
compose observation transforms, reward functors, and dataset/recorder
settings (auto-saving on episode completion).
- **extensions**: Optional[Dict[str, Any]] — arbitrary task-specific key/value
pairs (e.g. `success_threshold`, `control_frequency`) that are automatically
set on the config *and* bound to the environment instance.
- **filter_visual_rand** / **filter_dataset_saving**: booleans to disable
visual randomization or dataset saving for debugging purposes.
- **init_rollout_buffer**: bool — when true (or when a dataset manager is
present and dataset saving is enabled) the environment will initialize a
rollout buffer matching the observation/action spaces for episode
recording.
See `EmbodiedEnv` for usage patterns and the project documentation
for full examples showing how to declare environments from these configs.
"""
[docs]
@configclass
class EnvLightCfg:
direct: List[LightCfg] = []
# TODO: support more types of indirect light in the future.
indirect: dict[str, Any] | None = None
robot: RobotCfg = MISSING
control_parts: list[str] | None = None
"""List of robot parts to control. If None, all controllable joints will be used.
This is useful when we want to control only a subset of the robot joints for certain tasks or demonstrations.
"""
active_joint_ids: List[int] = []
"""List of active joint IDs for control. User also can directly specify the active joint IDs instead of control \
parts. This is useful when the control parts are not well defined or we want to have more fine-grained control.
"""
sensor: List[SensorCfg] = []
light: EnvLightCfg = EnvLightCfg()
background: List[RigidObjectCfg] = []
rigid_object: List[RigidObjectCfg] = []
rigid_object_group: List[RigidObjectGroupCfg] = []
articulation: List[ArticulationCfg] = []
events: Union[object, None] = None
"""Event settings. Defaults to None, in which case no events are applied through the event manager.
Please refer to the :class:`embodichain.lab.gym.managers.EventManager` class for more details.
"""
observations: Union[object, None] = None
"""Observation settings. Defaults to None, in which case no additional observations are applied through
the observation manager.
Please refer to the :class:`embodichain.lab.gym.managers.ObservationManager` class for more details.
"""
rewards: Union[object, None] = None
"""Reward settings. Defaults to None, in which case no reward computation is performed through
the reward manager.
Please refer to the :class:`embodichain.lab.gym.managers.RewardManager` class for more details.
"""
dataset: Union[object, None] = None
"""Dataset settings. Defaults to None, in which case no dataset collection is performed.
Please refer to the :class:`embodichain.lab.gym.managers.DatasetManager` class for more details.
"""
actions: Union[object, None] = None
"""Action manager settings. Defaults to None, in which case no action preprocessing is applied.
When configured, the ActionManager preprocesses raw policy actions (e.g., delta_qpos, eef_pose)
into robot control format.
Please refer to the :class:`embodichain.lab.gym.envs.managers.ActionManager` class for more details.
"""
extensions: Union[Dict[str, Any], None] = None
"""Extension parameters for task-specific configurations.
This field can be used to pass additional parameters that are specific to certain
environments or tasks without modifying the base configuration class. For example:
- success_threshold: Task-specific success distance threshold
- vr_joint_mapping: VR joint mapping for teleoperation
- control_frequency: Control frequency for VR teleoperation
Note: Action configuration (e.g., delta_qpos, scale) should use the ``actions``
field and ActionManager, not extensions.
"""
# Some helper attributes
filter_visual_rand: bool = False
"""Whether to filter out visual randomization
This is useful when we want to disable visual randomization for debug motion and physics issues.
"""
filter_dataset_saving: bool = False
"""Whether to filter out dataset saving
This is useful when we want to disable dataset saving for debug motion and physics issues.
If no dataset manager is configured, this flag will have no effect.
"""
init_rollout_buffer: bool = False
"""Whether to initialize the rollout buffer in the environment.
If filter_dataset_saving is False and a dataset manager is configured, the rollout buffer will be initialized by default
"""
record_trajectory: bool = False
"""Whether to record per-object states and pre-process actions.
Each saved row is a causal ``(state_t, action_t)`` pair, matching expert
trajectory frame alignment. Uses a per-env step counter so async parallel
environments are supported.
"""
trajectory_uids: list[str] | None = None
"""Optional allow-list of non-robot object uids to record. If None, all rigid
objects and articulations are recorded. The robot is always recorded."""
trajectory_save_dir: str | None = None
"""Directory for auto-saved trajectories. Defaults to
``<EMBODICHAIN_DEFAULT_DATA_ROOT>/trajectories/{run_id}/``."""
trajectory_auto_save: bool = True
"""If True (and record_trajectory is True), auto-save each env's trajectory to
``trajectory_save_dir`` at episode end and on close()."""
task_program: TaskProgramCfg | None = None
"""Optional declarative Task Program used to generate demo segments.
The program remains inert until :meth:`EmbodiedEnv.create_demo_segments`
requests an explicit environment compiler and bridge through the dedicated
hooks. No live provider, planner, or callable is stored in this config.
"""
[docs]
@register_env("EmbodiedEnv-v1")
class EmbodiedEnv(BaseEnv):
"""Embodied AI environment that is used to simulate the Embodied AI tasks.
Core simulation components for Embodied AI environments.
- sensor: The sensors used to perceive the environment, which could be attached to the agent or the environment.
- robot: The robot which will be used to interact with the environment.
- light: The lights in the environment, which could be used to illuminate the environment.
- indirect: the indirect light sources, such as ambient light, IBL, etc.
The indirect light sources are used for global illumination which affects the entire scene.
- direct: The direct light sources, such as point light, spot light, etc.
The direct light sources are used for local illumination which mainly affects the arena in the scene.
- background: Kinematic or Static rigid objects, such as obstacles or landmarks.
- rigid_object: Dynamic objects that can be interacted with.
- rigid_object_group: Groups of rigid objects that can be interacted with.
- deformable_object(TODO: supported in the future): Deformable volumes or surfaces (cloth) that can be interacted with.
- articulation: Articulated objects that can be manipulated, such as doors, drawers, etc.
- event manager: The event manager is used to manage the events in the environment, such as randomization,
perturbation, etc.
- observation manager: The observation manager is used to manage the observations in the environment,
such as depth, segmentation, etc.
- action bank: The action bank is used to manage the actions in the environment, such as action composition, action graph, etc.
- affordance_datas: The affordance data that can be used to store the intermediate results or information
"""
_defer_initialization_summary: bool = True
_manager_summary_fields: tuple[tuple[str, str], ...] = (
("EventManager", "event_manager"),
("ObservationManager", "observation_manager"),
("RewardManager", "reward_manager"),
("ActionManager", "action_manager"),
("DatasetManager", "dataset_manager"),
)
@classmethod
def __init_subclass__(cls, **kwargs):
"""Automatically wrap subclass demo-action builders with shape checks.
Any subclass overriding ``create_demo_action_list`` will be wrapped so its
returned action sequence is validated and, when possible, converted to the
environment action dimension.
"""
super().__init_subclass__(**kwargs)
method = cls.__dict__.get("create_demo_action_list")
if method is None or getattr(method, "_demo_action_shape_wrapped", False):
return
@wraps(method)
def wrapped_create_demo_action_list(self, *args, **kwargs):
action_list = method(self, *args, **kwargs)
return self._normalize_demo_action_list(action_list)
wrapped_create_demo_action_list._demo_action_shape_wrapped = True
setattr(cls, "create_demo_action_list", wrapped_create_demo_action_list)
[docs]
def __init__(
self,
cfg: EmbodiedEnvCfg,
*,
task_program_adapter_factory: TaskProgramAdapterFactory | None = None,
**kwargs,
):
if task_program_adapter_factory is not None:
from embodichain.lab.task_program.integrations import (
TaskProgramAdapterFactory,
)
if not isinstance(
task_program_adapter_factory,
TaskProgramAdapterFactory,
):
raise TypeError(
"task_program_adapter_factory must implement "
"TaskProgramAdapterFactory or be None."
)
self.affordance_datas = {}
self.action_bank = None
self._task_program_adapter: TaskProgramEnvironmentAdapter | None = None
self._active_task_program_bridge: TaskProgramDemoBridge | None = None
extensions = getattr(cfg, "extensions", {}) or {}
for name, value in extensions.items():
setattr(cfg, name, value)
setattr(self, name, value)
self.event_manager: EventManager | None = None
self.observation_manager: ObservationManager | None = None
self.reward_manager: RewardManager | None = None
self.action_manager: ActionManager | None = None
self.dataset_manager: DatasetManager | None = None
super().__init__(cfg, **kwargs)
if task_program_adapter_factory is not None:
from embodichain.lab.task_program.integrations import (
TaskProgramEnvironmentAdapter,
)
adapter = task_program_adapter_factory.create_adapter(self)
if type(adapter) is not TaskProgramEnvironmentAdapter:
raise TypeError(
"TaskProgramAdapterFactory.create_adapter() must return "
"exactly TaskProgramEnvironmentAdapter."
)
self._task_program_adapter = adapter
dataset_terms = getattr(self.cfg.dataset, "__dict__", self.cfg.dataset)
if dataset_terms and not self.cfg.filter_dataset_saving:
self.dataset_manager = DatasetManager(self.cfg.dataset, self)
self.cfg.init_rollout_buffer = True
# Rollout buffer for episode data collection.
# The shape of the buffer is (num_envs, max_episode_steps, *data_shape) for each key.
# The default key in the buffer are:
# - obs: the observation returned by the environment.
# - action: the action applied to the environment.
# - reward: the reward returned by the environment.
# TODO: we may add more keys and make the buffer extensible in the future.
# This buffer should also be support initialized from outside of the environment.
# For example, a shared rollout buffer initialized in model training process and passed to the environment for data collection.
self.rollout_buffer: TensorDict | None = None
self._max_rollout_steps = 0
self._rollout_buffer_mode: str | None = None
if self.cfg.init_rollout_buffer:
self.rollout_buffer = init_rollout_buffer_from_gym_space(
obs_space=self.observation_space,
action_space=self.action_space,
max_episode_steps=self.max_episode_steps,
num_envs=self.num_envs,
device=self.device,
)
self._max_rollout_steps = self.rollout_buffer.shape[1]
self._rollout_buffer_mode = "expert"
# Dedicated per-env trajectory buffer (states + actions). Decoupled from
# rollout_buffer so async parallel envs and ActionManager are supported.
self._traj_buffer: TensorDict | None = None
self._traj_steps: torch.Tensor | None = None
self._traj_raw_action: EnvAction | None = None
self._traj_save_count = 0
self._traj_run_id = datetime.now().strftime("%Y%m%d_%H%M%S")
if self.cfg.record_trajectory:
self._traj_buffer = build_trajectory_buffer(
env=self,
max_steps=self.max_episode_steps,
num_envs=self.num_envs,
device=self.device,
uids=self.cfg.trajectory_uids,
action_space=self.action_space,
)
self._traj_steps = torch.zeros(
self.num_envs, dtype=torch.long, device=self.device
)
self.rollout_steps = torch.zeros(
self.num_envs, dtype=torch.long, device=self.device
)
self._demo_steps = torch.zeros(
self.num_envs, dtype=torch.long, device=self.device
)
self.current_rollout_step = 0
# Segment recording is intentionally separate from task planning. The
# common demo executor updates this context while the regular rollout
# writer turns it into per-frame annotations.
self._demo_episode_index = 0
self._demo_execution_cfg = DemoExecutionCfg()
self._demo_attempt_id = 0
self._demo_continuity_id = 0
self._demo_program_run_id = "0:0"
self._demo_active_segment_id = 0
self._demo_active_segment_ids = torch.zeros(
self.num_envs, dtype=torch.long, device=self.device
)
self._demo_active_mask = torch.ones(
self.num_envs, dtype=torch.bool, device=self.device
)
self._demo_segment_participants = self._demo_active_mask.clone()
self._demo_active_segment_start_steps = self._demo_steps.clone()
self._demo_active_rollout_start_steps = self.rollout_steps.clone()
self._demo_episode_metadata: list[dict[str, Any]] = [
self._new_demo_episode_metadata(env_id) for env_id in range(self.num_envs)
]
self.episode_success_status: torch.Tensor = torch.zeros(
self.num_envs, dtype=torch.bool, device=self.device
)
self._closed = False
self._close_error: BaseException | None = None
self._close_lock = threading.RLock()
all_env_ids = torch.arange(self.num_envs, device=self.device)
self._seed_recording_state(self._init_raw_obs, all_env_ids)
self._log_initialization_summary()
def _extra_initialization_summary_lines(self) -> list[str]:
"""Build manager and functor details for the initialization summary."""
manager_summaries: list[tuple[str, list[tuple[str, list[str]]] | None, int]] = (
[]
)
active_manager_count = 0
total_functor_count = 0
for manager_name, attribute_name in self._manager_summary_fields:
manager = getattr(self, attribute_name, None)
if manager is None:
manager_summaries.append((manager_name, None, 0))
continue
groups = self._manager_functor_groups(manager_name, manager)
functor_count = sum(len(names) for _, names in groups)
manager_summaries.append((manager_name, groups, functor_count))
active_manager_count += 1
total_functor_count += functor_count
functor_noun = "functor" if total_functor_count == 1 else "functors"
lines = [
f"├─ Managers ({active_manager_count}/{len(manager_summaries)} active, "
f"{total_functor_count} {functor_noun})"
]
for manager_name, groups, functor_count in manager_summaries:
if groups is None:
lines.append(
self._format_initialization_summary_row(manager_name, "disabled")
)
continue
manager_functor_noun = "functor" if functor_count == 1 else "functors"
lines.append(
self._format_initialization_summary_row(
manager_name, f"{functor_count} {manager_functor_noun}"
)
)
for mode, names in groups:
lines.append(
self._format_initialization_summary_row(
mode, ", ".join(names), indent=1
)
)
return lines
@staticmethod
def _manager_functor_groups(
manager_name: str, manager: object
) -> list[tuple[str, list[str]]]:
"""Normalize a manager's active functors into display groups."""
active_functors = manager.active_functors
if isinstance(active_functors, Mapping):
return [
(str(mode), [str(name) for name in names])
for mode, names in active_functors.items()
]
names = [str(name) for name in active_functors]
if manager_name != "ActionManager":
return [("terms", names)] if names else []
get_terms_by_mode = getattr(manager, "get_terms_by_mode", None)
if not callable(get_terms_by_mode):
return [("terms", names)] if names else []
groups: list[tuple[str, list[str]]] = []
grouped_names: set[str] = set()
for mode in ("pre", "post"):
mode_names = [str(name) for name, _ in get_terms_by_mode(mode)]
if mode_names:
groups.append((mode, mode_names))
grouped_names.update(mode_names)
remaining_names = [name for name in names if name not in grouped_names]
if remaining_names:
groups.append(("terms", remaining_names))
return groups
[docs]
def reset(
self, seed: int | None = None, options: dict | None = None
) -> tuple[EnvObs, Dict]:
"""Reset environments and seed pre-action recording state.
Expert frames must pair the observation before an action with that
action. The base reset computes the authoritative post-reset
observation, so recording is seeded only after it returns.
Args:
seed: Optional random seed forwarded to :class:`BaseEnv`.
options: Reset options. ``reset_ids`` may select only some vector
environment rows. ``commit_env_ids`` may select a subset of
the reset rows whose pending dataset episodes are persisted.
Returns:
The reset observation and info dictionary.
"""
obs, info = super().reset(seed=seed, options=options)
self._active_task_program_bridge = None
if options is None or "reset_ids" not in options:
reset_ids = torch.arange(self.num_envs, device=self.device)
else:
reset_ids = torch.as_tensor(
options["reset_ids"], dtype=torch.long, device=self.device
).reshape(-1)
self._seed_recording_state(obs, reset_ids)
return obs, info
def _seed_recording_state(self, obs: EnvObs, env_ids: torch.Tensor) -> None:
"""Seed all enabled recorders from the current environment state."""
self._seed_expert_observations(obs, env_ids)
self._seed_trajectory_states(env_ids)
def _seed_expert_observations(self, obs: EnvObs, env_ids: torch.Tensor) -> None:
"""Copy current observations into pending expert-transition slots."""
if (
self.rollout_buffer is None
or getattr(self, "_rollout_buffer_mode", "expert") == "rl"
or env_ids.numel() == 0
):
return
eligible = self.rollout_steps[env_ids] < self._max_rollout_steps
env_ids = env_ids[eligible]
if env_ids.numel() == 0:
return
step_ids = self.rollout_steps[env_ids]
buffer_device = self.rollout_buffer.device
buffer_env_ids = env_ids.to(buffer_device)
buffer_step_ids = step_ids.to(buffer_device)
obs_device = getattr(obs, "device", None) or self.device
obs_env_ids = env_ids.to(obs_device)
self.rollout_buffer["obs"][buffer_env_ids, buffer_step_ids] = obs[
obs_env_ids
].to(buffer_device)
def _seed_trajectory_states(self, env_ids: torch.Tensor) -> None:
"""Copy current states into pending trajectory-transition slots."""
if self._traj_buffer is None or env_ids.numel() == 0:
return
env_ids = env_ids.to(device=self.device, dtype=torch.long)
eligible = self._traj_steps[env_ids] < self._traj_buffer.shape[1]
env_ids = env_ids[eligible]
if env_ids.numel() == 0:
return
step_ids = self._traj_steps[env_ids]
capture_trajectory_state(self, self._traj_buffer["states"], env_ids, step_ids)
[docs]
def set_rollout_buffer(self, rollout_buffer: TensorDict) -> None:
"""Set the rollout buffer for episode data collection.
This function can be used to set the rollout buffer from outside of the environment,
such as a shared rollout buffer initialized in model training process and passed to the environment for data collection.
Args:
rollout_buffer (TensorDict): The rollout buffer to be set. RL
rollouts use a uniform `[num_envs, time + 1]` layout so all
fields share the same batch shape; the last slot of
transition-only fields is reserved as padding. Expert buffers
keep the legacy `[num_envs, time]` batch layout.
"""
if rollout_buffer.shape[0] != self.num_envs:
raise ValueError(
"Rollout buffer rows must match env.num_envs: "
f"got {rollout_buffer.shape[0]} rows for {self.num_envs} envs."
)
self.rollout_buffer = rollout_buffer
self._rollout_buffer_mode = self._infer_rollout_buffer_mode(rollout_buffer)
if self._rollout_buffer_mode == "rl":
batch_size = self.rollout_buffer.batch_size
if len(batch_size) != 2:
message = (
f"Invalid RL rollout buffer batch size: {batch_size}. "
"Expected a 2D batch layout [num_envs, time + 1] for RL rollouts."
)
logger.log_error(message)
raise ValueError(message)
self._max_rollout_steps = batch_size[1] - 1
else:
if len(rollout_buffer.shape) != 2:
logger.log_error(
f"Invalid rollout buffer shape: {rollout_buffer.shape}. The expected shape is (num_envs, max_episode_steps) for each key."
)
self._max_rollout_steps = self.rollout_buffer.shape[1]
self.rollout_steps.zero_()
self.current_rollout_step = 0
if self._rollout_buffer_mode != "rl":
env_ids = torch.arange(self.num_envs, device=self.device)
self._clear_expert_rollout_rows(env_ids)
self._seed_expert_observations(self.get_obs(), env_ids)
def _init_sim_state(self, **kwargs):
"""Initialize the simulation state at the beginning of scene creation."""
self._apply_functor_filter()
# create event manager
self.cfg: EmbodiedEnvCfg
if self.cfg.events:
self.event_manager = EventManager(self.cfg.events, self)
# perform events at the start of the simulation
if "startup" in self.event_manager.available_modes:
self.event_manager.apply(mode="startup")
if self.cfg.observations:
self.observation_manager = ObservationManager(self.cfg.observations, self)
if self.cfg.rewards:
self.reward_manager = RewardManager(self.cfg.rewards, self)
if self.cfg.actions:
self.action_manager = ActionManager(self.cfg.actions, self)
# Override action space to match ActionManager output dim.
self.single_action_space = self.action_manager.single_action_space
def _apply_functor_filter(self) -> None:
"""Apply functor filters to the environment components based on configuration.
This method is used to filter out certain components of the environment, such as visual randomization,
based on the configuration settings. For example, if `filter_visual_rand` is set to True in the configuration,
all visual randomization functors will be removed from the event manager.
"""
from embodichain.utils.module_utils import get_all_exported_items_from_module
from embodichain.lab.gym.envs.managers.cfg import EventCfg
functors_to_remove = {
name
for name in get_all_exported_items_from_module(
"embodichain.lab.gym.envs.managers.randomization.visual"
)
if name.startswith("randomize_")
}
if self.cfg.filter_visual_rand and self.cfg.events:
# Iterate through all attributes of the events object
for attr_name in dir(self.cfg.events):
attr = getattr(self.cfg.events, attr_name)
if isinstance(attr, EventCfg):
if attr.func.__name__ in functors_to_remove:
logger.log_info(
f"Filtering out visual randomization functor: {attr.func.__name__}"
)
setattr(self.cfg.events, attr_name, None)
def _init_action_bank(
self, action_bank_cls: ActionBank, action_config: Dict[str, Any]
):
"""
Initialize action bank and parse action graph structure.
Args:
action_bank_cls: The ActionBank class for this environment.
action_config: The configuration dict for the action bank.
"""
self.action_bank = action_bank_cls(action_config)
try:
this_class_name = self.action_bank.__class__.__name__
node_func = {}
edge_func = {}
for class_name in [this_class_name, ActionBank.__name__]:
node_func.update(get_func_tag("node").functions.get(class_name, {}))
edge_func.update(get_func_tag("edge").functions.get(class_name, {}))
except KeyError as e:
raise KeyError(
f"Function tag for {e} not found in action bank function registry."
)
self.graph_compose, jobs_data, jobkey2index = self.action_bank.parse_network(
node_functions=node_func, edge_functions=edge_func, vis_graph=False
)
self.packages = self.action_bank.gantt(
tasks_data=jobs_data, taskkey2index=jobkey2index, vis=False
)
[docs]
def set_affordance(self, key: str, value: Any):
"""
Set an affordance value by key.
Args:
key (str): The affordance key.
value (Any): The affordance value.
"""
self.affordance_datas[key] = value
[docs]
def get_affordance(self, key: str, default: Any = None):
"""
Get an affordance value by key.
Args:
key (str): The affordance key.
default (Any, optional): Default value if key not found.
Returns:
Any: The affordance value or default.
"""
return self.affordance_datas.get(key, default)
def _hook_after_sim_step(
self,
obs: EnvObs,
action: EnvAction,
rewards: torch.Tensor,
dones: torch.Tensor,
info: Dict,
**kwargs,
):
# TODO: We may make the data collection customizable for rollout buffer.
if self.rollout_buffer is not None:
with self._profiler.section("rollout_write"):
if self._rollout_buffer_mode == "rl":
if self.current_rollout_step < self._max_rollout_steps:
self._write_rl_rollout_step(
obs=obs,
rewards=rewards,
dones=dones,
terminateds=kwargs.get("terminateds"),
truncateds=kwargs.get("truncateds"),
)
else:
logger.log_warning(
f"Current rollout step {self.current_rollout_step} exceeds max rollout steps {self._max_rollout_steps}. "
"Data will not be recorded in the rollout buffer."
)
self.current_rollout_step += 1
else:
self._write_episode_rollout_step(
obs=obs,
action=action,
rewards=rewards,
terminateds=kwargs.get("terminateds"),
truncateds=kwargs.get("truncateds"),
)
demo_steps = getattr(self, "_demo_steps", None)
if demo_steps is not None:
active_mask = getattr(self, "_demo_active_mask", None)
if active_mask is None:
demo_steps += 1
else:
demo_steps += active_mask.to(demo_steps.device, dtype=demo_steps.dtype)
with self._profiler.section("trajectory_write"):
self._write_trajectory_step()
self._update_episode_success_status(info, dones)
def _update_episode_success_status(
self, info: Dict[str, Any], dones: torch.Tensor
) -> None:
"""Update terminal success without mutating already frozen demo rows."""
if "success" not in info:
return
update_mask = dones.to(
device=self.episode_success_status.device, dtype=torch.bool
)
if getattr(self, "_demo_no_auto_reset", False):
active_mask = getattr(self, "_demo_active_mask", None)
if active_mask is not None:
update_mask = update_mask & active_mask.to(update_mask.device)
success = torch.as_tensor(
info["success"],
dtype=torch.bool,
device=self.episode_success_status.device,
)
self.episode_success_status[update_mask] = success[update_mask]
def _extend_obs(self, obs: EnvObs, **kwargs) -> EnvObs:
if self.observation_manager:
with self._profiler.section("obs_compute"):
obs = self.observation_manager.compute(obs)
return obs
def _extend_reward(
self,
rewards: torch.Tensor,
obs: EnvObs,
action: EnvAction,
info: Dict[str, Any],
**kwargs,
) -> torch.Tensor:
if self.reward_manager:
with self._profiler.section("reward_compute"):
extra_rewards, reward_info = self.reward_manager.compute(
obs=obs, action=action, info=info
)
info["rewards"] = reward_info
# Add manager terms to base reward from get_reward() so task reward is kept
rewards = rewards + extra_rewards
return rewards
def _prepare_scene(self, **kwargs) -> None:
self._setup_lights()
self._setup_background()
self._setup_interactive_objects()
def _update_sim_state(self, **kwargs) -> None:
"""Perform the simulation step and apply events if configured.
The events manager applies its functors after physics simulation and rendering,
and before the observation and reward computation (if applicable).
"""
if self.cfg.events:
if "interval" in self.event_manager.available_modes:
with self._profiler.section("event_interval"):
self.event_manager.apply(mode="interval")
def _initialize_episode(
self, env_ids: Sequence[int] | None = None, **kwargs
) -> None:
logger.log_debug(f"Initializing episode for env_ids: {env_ids}", color="blue")
save_data = kwargs.get("save_data", True)
# Determine which environments to process
status_device = self.episode_success_status.device
if env_ids is None:
env_ids_to_process = torch.arange(self.num_envs, device=status_device)
elif isinstance(env_ids, torch.Tensor):
env_ids_to_process = env_ids.to(device=status_device, dtype=torch.long)
else:
env_ids_to_process = torch.tensor(
list(env_ids), device=status_device, dtype=torch.long
)
commit_env_ids = kwargs.get("commit_env_ids")
if commit_env_ids is None:
env_ids_to_commit = (
env_ids_to_process
if save_data
else torch.empty(0, device=status_device, dtype=torch.long)
)
elif isinstance(commit_env_ids, torch.Tensor):
env_ids_to_commit = commit_env_ids.to(
device=status_device, dtype=torch.long
).reshape(-1)
else:
env_ids_to_commit = torch.tensor(
list(commit_env_ids), device=status_device, dtype=torch.long
)
if commit_env_ids is not None and env_ids_to_commit.numel() > 0:
if torch.unique(env_ids_to_commit).numel() != env_ids_to_commit.numel():
raise ValueError("commit_env_ids must not contain duplicate rows.")
if not bool(torch.isin(env_ids_to_commit, env_ids_to_process).all().item()):
raise ValueError(
"commit_env_ids must be a subset of the rows being reset."
)
# Save dataset before clearing buffers for environments that are being reset
if env_ids_to_commit.numel() > 0 and self.dataset_manager:
if "save" in self.dataset_manager.available_modes:
if commit_env_ids is not None:
env_ids_to_save = env_ids_to_commit
elif self.dataset_manager.save_failed_episodes:
env_ids_to_save = env_ids_to_process
else:
successful_envs = self.episode_success_status | self._task_success
fragment_envs = torch.tensor(
[
EmbodiedEnv._demo_fragment_row_is_persistable(
self, int(env_id)
)
for env_id in env_ids_to_process.cpu().tolist()
],
dtype=torch.bool,
device=status_device,
)
env_ids_to_save = env_ids_to_process[
successful_envs[env_ids_to_process] | fragment_envs
]
if env_ids_to_save.numel() > 0:
with self._profiler.section("dataset_save"):
self.dataset_manager.apply(
mode="save",
env_ids=env_ids_to_save,
)
# Save recorded camera data before resetting
if self.cfg.events and self.event_manager is not None:
from embodichain.lab.gym.envs.managers.record import record_camera_data
with self._profiler.section("record_camera_save"):
for mode_cfgs in self.event_manager._mode_functor_cfgs.values():
for functor_cfg in mode_cfgs:
if isinstance(functor_cfg.func, record_camera_data):
if save_data:
functor_cfg.func.save_and_clear(
env_ids=env_ids_to_process
)
else:
functor_cfg.func.discard_and_clear(
env_ids=env_ids_to_process
)
# Auto-save + reset the per-env trajectory buffer for environments being
# reset. Use getattr so this no-ops on envs/subclasses that don't allocate
# a _traj_buffer (e.g. unit-test stubs of _initialize_episode).
_traj_buffer = getattr(self, "_traj_buffer", None)
if (
save_data
and _traj_buffer is not None
and getattr(self.cfg, "trajectory_auto_save", False)
):
with self._profiler.section("trajectory_save"):
for env_id in env_ids_to_process.tolist():
self._save_trajectory_for_env(env_id)
_traj_steps = getattr(self, "_traj_steps", None)
if _traj_steps is not None:
_traj_steps[env_ids_to_process] = 0
# Clear episode buffers only after every recorder has consumed them.
if self.rollout_buffer is not None and self._rollout_buffer_mode != "rl":
self._clear_expert_rollout_rows(env_ids_to_process)
rollout_steps = getattr(self, "rollout_steps", None)
if rollout_steps is not None:
rollout_ids = env_ids_to_process.to(rollout_steps.device)
rollout_steps[rollout_ids] = 0
self.current_rollout_step = int(rollout_steps.max().item())
episode_metadata = getattr(self, "_demo_episode_metadata", None)
if episode_metadata is not None:
for env_id in env_ids_to_process.cpu().tolist():
episode_metadata[env_id] = self._new_demo_episode_metadata(env_id)
active_segment_ids = getattr(self, "_demo_active_segment_ids", None)
if active_segment_ids is not None:
demo_ids = env_ids_to_process.to(active_segment_ids.device)
active_segment_ids[demo_ids] = 0
self._demo_active_mask[demo_ids] = True
self._demo_segment_participants[demo_ids] = False
self._demo_active_segment_start_steps[demo_ids] = 0
self._demo_active_rollout_start_steps[demo_ids] = 0
self._demo_steps[demo_ids] = 0
self.episode_success_status[env_ids_to_process] = False
# apply events such as randomization for environments that need a reset
if self.cfg.events:
if "reset" in self.event_manager.available_modes:
with self._profiler.section("event_reset"):
self.event_manager.apply(mode="reset", env_ids=env_ids)
# reset observation manager for environments that need a reset
# This clears any cached data in observation functors (e.g., physics attributes)
if self.cfg.observations:
with self._profiler.section("obs_reset"):
self.observation_manager.reset(env_ids=env_ids)
# reset reward manager for environments that need a reset
if self.cfg.rewards:
with self._profiler.section("reward_reset"):
self.reward_manager.reset(env_ids=env_ids)
# Dataset saving can be disabled while the dataset configuration remains
# present. In that mode no DatasetManager is created in __init__, so
# reset must not dereference the optional manager.
if self.cfg.dataset and self.dataset_manager is not None:
with self._profiler.section("dataset_reset"):
self.dataset_manager.reset(env_ids=env_ids)
def _clear_expert_rollout_rows(self, env_ids: torch.Tensor) -> None:
"""Invalidate selected expert-buffer rows without clearing large frames."""
if self.rollout_buffer is None or len(env_ids) == 0:
return
buffer_ids = env_ids.to(self.rollout_buffer.device, dtype=torch.long)
if "valid" not in self.rollout_buffer.keys():
# Preserve schema-v1 behavior for external buffers that have no
# validity mask and therefore cannot hide a stale tail.
for key in self.rollout_buffer.keys(include_nested=True, leaves_only=True):
self.rollout_buffer[key][buffer_ids] = 0
return
for key in (
"valid",
"segment_start",
"segment_end",
"segment_accepted",
"terminated",
"truncated",
):
if key in self.rollout_buffer.keys():
self.rollout_buffer[key][buffer_ids] = False
for key in (
"episode_step",
"segment_id",
"segment_step",
"segment_attempt_id",
"continuity_id",
):
if key in self.rollout_buffer.keys():
self.rollout_buffer[key][buffer_ids] = -1
def _demo_fragment_row_is_persistable(self, env_id: int) -> bool:
"""Return whether one fragment-mode row owns an eligible frame span."""
episode_metadata = getattr(self, "_demo_episode_metadata", None)
if episode_metadata is None or env_id >= len(episode_metadata):
return False
metadata = episode_metadata[env_id]
if metadata.get("output_mode") != "segment_fragments":
return False
include_failed = bool(metadata.get("save_failed_fragments", False))
for segment in metadata.get("segments", []):
if int(segment.get("end_step", 0)) <= int(segment.get("start_step", 0)):
continue
if bool(segment.get("success", False)) or include_failed:
return True
return False
def _new_demo_episode_metadata(self, env_id: int) -> dict[str, Any]:
"""Create an empty metadata record for one environment row."""
execution_cfg = getattr(self, "_demo_execution_cfg", None)
if execution_cfg is None:
execution_cfg = DemoExecutionCfg()
attempt_id = int(getattr(self, "_demo_attempt_id", 0))
return {
"schema_version": DEMO_SCHEMA_VERSION,
"episode_index": int(getattr(self, "_demo_episode_index", 0)),
"output_mode": execution_cfg.mode,
"save_failed_fragments": execution_cfg.save_failed_fragments,
"attempt_id": attempt_id,
"continuity_id": int(getattr(self, "_demo_continuity_id", 0)),
"program_run_id": str(
getattr(
self,
"_demo_program_run_id",
f"{int(getattr(self, '_demo_episode_index', 0))}:{attempt_id}",
)
),
"env_id": env_id,
"length": 0,
"completed": False,
"success": False,
"terminated": False,
"truncated": False,
"terminal_reason": "unknown",
"segments": [],
}
def _begin_demo_episode_recording(
self,
episode_index: int = 0,
execution_cfg: DemoExecutionCfg | None = None,
attempt_id: int = 0,
) -> None:
"""Start annotation metadata for a new demonstration episode."""
if execution_cfg is None:
execution_cfg = DemoExecutionCfg()
self._demo_episode_index = episode_index
self._demo_execution_cfg = execution_cfg
self._demo_attempt_id = attempt_id
self._demo_continuity_id = 0
self._demo_program_run_id = f"{episode_index}:{attempt_id}"
self._demo_active_segment_id = 0
self._demo_active_segment_ids.zero_()
self._demo_active_mask.fill_(True)
self._demo_segment_participants.fill_(False)
self._demo_active_segment_start_steps = self._demo_steps.clone()
self._demo_active_rollout_start_steps = self.rollout_steps.clone()
self._demo_episode_metadata = [
self._new_demo_episode_metadata(env_id) for env_id in range(self.num_envs)
]
def _begin_demo_segment_recording(
self, segment_id: int, segment: DemoSegment
) -> None:
"""Set the segment context used by subsequent rollout writes."""
self._demo_active_segment_id = segment_id
self._demo_segment_participants = self._demo_active_mask.clone()
self._demo_active_segment_ids[self._demo_segment_participants] = segment_id
self._demo_active_segment_start_steps = self._demo_steps.clone()
self._demo_active_rollout_start_steps = self.rollout_steps.clone()
def _set_demo_active_mask(self, active_mask: Sequence[bool]) -> None:
"""Set rows that still participate in the current demo episode.
The executor updates this sticky mask after each terminal transition.
Recording hooks use it to freeze completed rows while unfinished rows
continue on the shared vector-environment clock.
Args:
active_mask: One liveness flag per parallel environment.
Raises:
ValueError: If the mask length does not match ``num_envs``.
"""
mask = torch.as_tensor(
active_mask, dtype=torch.bool, device=self._demo_active_mask.device
).reshape(-1)
if mask.numel() != self.num_envs:
raise ValueError(
f"Expected {self.num_envs} demo activity flags, got {mask.numel()}."
)
self._demo_active_mask.copy_(mask)
def _end_demo_segment_recording(self, result: DemoSegmentResult) -> None:
"""Close the active segment span and mark its final valid frame."""
for env_id in range(self.num_envs):
is_participant = (
result.active[env_id]
if result.active
else bool(self._demo_segment_participants[env_id])
)
if not is_participant:
continue
start = int(self._demo_active_segment_start_steps[env_id].item())
end = int(self._demo_steps[env_id].item())
rollout_start = int(self._demo_active_rollout_start_steps[env_id].item())
rollout_end = int(self.rollout_steps[env_id].item())
if (
self.rollout_buffer is not None
and "segment_end" in self.rollout_buffer.keys()
and rollout_end > rollout_start
):
self.rollout_buffer["segment_end"][env_id, rollout_end - 1] = True
if (
self.rollout_buffer is not None
and "segment_accepted" in self.rollout_buffer.keys()
and rollout_end > rollout_start
):
accepted = (
result.successes[env_id] if result.successes else result.success
)
self.rollout_buffer["segment_accepted"][
env_id, rollout_start:rollout_end
] = accepted
metadata = result.to_metadata(env_id if result.start_steps else None)
metadata["start_step"] = start
metadata["end_step"] = end
self._demo_episode_metadata[env_id]["segments"].append(metadata)
def _end_demo_episode_recording(self, result: DemoEpisodeResult) -> None:
"""Finalize per-environment metadata after demonstration execution."""
for env_id in range(self.num_envs):
metadata = self._demo_episode_metadata[env_id]
length = (
result.lengths[env_id]
if result.lengths
else int(self._demo_steps[env_id].item())
)
completed = (
result.completed_by_env[env_id]
if result.completed_by_env
else result.completed
)
terminal_reason = (
result.terminal_reasons[env_id]
if result.terminal_reasons
else result.terminal_reason
)
metadata.update(
{
"episode_index": result.episode_index,
"length": length,
"completed": completed,
"success": result.success[env_id],
"terminated": result.terminated[env_id],
"truncated": result.truncated[env_id],
"terminal_reason": terminal_reason,
}
)
def _infer_rollout_buffer_mode(self, rollout_buffer: TensorDict) -> str:
"""Infer whether the rollout buffer is expert recording or RL training data."""
if {
"obs",
"action",
"reward",
"done",
"value",
"terminated",
"truncated",
}.issubset(set(rollout_buffer.keys())):
return "rl"
return "expert"
def _write_episode_rollout_step(
self,
obs: EnvObs,
action: EnvAction,
rewards: torch.Tensor,
terminateds: torch.Tensor | None = None,
truncateds: torch.Tensor | None = None,
) -> None:
"""Complete one causally aligned expert transition per active env.
The observation at each cursor is seeded before its action is applied.
This method fills transition fields at that cursor, then seeds the
returned post-action observation as the next pending pre-action state.
"""
buffer_device = self.rollout_buffer.device
active = self.rollout_steps < self._max_rollout_steps
demo_active = getattr(self, "_demo_active_mask", None)
if demo_active is not None:
active &= demo_active.to(active.device)
env_ids = active.nonzero(as_tuple=False).squeeze(-1)
if env_ids.numel() == 0:
logger.log_warning(
"No active expert rollout row can accept another frame; "
"new frames are dropped."
)
return
step_ids = self.rollout_steps[env_ids]
buffer_env_ids = env_ids.to(buffer_device)
buffer_step_ids = step_ids.to(buffer_device)
if isinstance(action, TensorDict):
action_to_store = (
action["qpos"]
if "qpos" in action
else (action["qvel"] if "qvel" in action else action["qf"])
)
elif isinstance(action, torch.Tensor):
action_to_store = action
else:
logger.log_warning(
f"Unexpected action type {type(action)} in _hook_after_sim_step; "
"skipping action storage in rollout buffer."
)
action_to_store = None
if action_to_store is not None:
self.rollout_buffer["actions"][buffer_env_ids, buffer_step_ids] = (
action_to_store[env_ids].to(buffer_device)
)
self.rollout_buffer["rewards"][buffer_env_ids, buffer_step_ids] = rewards[
env_ids
].to(buffer_device)
segment_start_steps = getattr(
self,
"_demo_active_rollout_start_steps",
self._demo_active_segment_start_steps,
)[env_ids]
segment_steps = step_ids - segment_start_steps
buffer_keys = set(self.rollout_buffer.keys())
if "valid" in buffer_keys:
self.rollout_buffer["valid"][buffer_env_ids, buffer_step_ids] = True
if "episode_step" in buffer_keys:
self.rollout_buffer["episode_step"][
buffer_env_ids, buffer_step_ids
] = buffer_step_ids
if "segment_id" in buffer_keys:
active_segment_ids = getattr(self, "_demo_active_segment_ids", None)
segment_ids = (
active_segment_ids[env_ids].to(buffer_device)
if active_segment_ids is not None
else self._demo_active_segment_id
)
self.rollout_buffer["segment_id"][
buffer_env_ids, buffer_step_ids
] = segment_ids
if "segment_step" in buffer_keys:
self.rollout_buffer["segment_step"][buffer_env_ids, buffer_step_ids] = (
segment_steps.to(buffer_device)
)
if "segment_start" in buffer_keys:
self.rollout_buffer["segment_start"][buffer_env_ids, buffer_step_ids] = (
segment_steps.to(buffer_device) == 0
)
if "segment_accepted" in buffer_keys:
# Acceptance is unknown until the segment validator commits. The
# end-segment hook fills this complete span retroactively.
self.rollout_buffer["segment_accepted"][
buffer_env_ids, buffer_step_ids
] = False
if "segment_attempt_id" in buffer_keys:
self.rollout_buffer["segment_attempt_id"][
buffer_env_ids, buffer_step_ids
] = int(getattr(self, "_demo_attempt_id", 0))
if "continuity_id" in buffer_keys:
self.rollout_buffer["continuity_id"][buffer_env_ids, buffer_step_ids] = int(
getattr(self, "_demo_continuity_id", 0)
)
if terminateds is None:
terminateds = torch.zeros(
self.num_envs, dtype=torch.bool, device=self.device
)
if truncateds is None:
truncateds = torch.zeros(
self.num_envs, dtype=torch.bool, device=self.device
)
terminal = terminateds.to(self.device) | truncateds.to(self.device)
if "terminated" in buffer_keys:
self.rollout_buffer["terminated"][buffer_env_ids, buffer_step_ids] = (
terminateds[env_ids].to(buffer_device)
)
if "truncated" in buffer_keys:
self.rollout_buffer["truncated"][buffer_env_ids, buffer_step_ids] = (
truncateds[env_ids].to(buffer_device)
)
if "segment_end" in buffer_keys:
self.rollout_buffer["segment_end"][buffer_env_ids, buffer_step_ids] = (
terminal[env_ids].to(buffer_device)
)
self.rollout_steps[env_ids] += 1
next_env_ids = env_ids[self.rollout_steps[env_ids] < self._max_rollout_steps]
self._seed_expert_observations(obs, next_env_ids)
self.current_rollout_step = int(self.rollout_steps.max().item())
def _write_trajectory_step(self) -> None:
"""Complete one pre-action ``state`` + ``action`` trajectory row."""
if self._traj_buffer is None:
return
max_steps = self._traj_buffer.shape[1]
step = self._traj_steps
mask = step < max_steps
demo_active = getattr(self, "_demo_active_mask", None)
if demo_active is not None:
mask &= demo_active.to(mask.device)
idx = mask.nonzero(as_tuple=False).squeeze(-1)
if idx.numel() == 0:
return
st = step[idx]
if self._traj_raw_action is not None:
self._traj_buffer["actions"][idx, st] = self._traj_raw_action[idx]
self._traj_steps[idx] += 1
next_env_ids = idx[self._traj_steps[idx] < self._traj_buffer.shape[1]]
self._seed_trajectory_states(next_env_ids)
self._traj_raw_action = None
def _write_rl_rollout_step(
self,
obs: EnvObs,
rewards: torch.Tensor,
dones: torch.Tensor,
terminateds: torch.Tensor | None,
truncateds: torch.Tensor | None,
) -> None:
"""Write environment-side fields into an externally managed RL rollout buffer."""
buffer_device = self.rollout_buffer.device
self.rollout_buffer["reward"][: self.num_envs, self.current_rollout_step].copy_(
rewards.to(buffer_device), non_blocking=True
)
self.rollout_buffer["done"][: self.num_envs, self.current_rollout_step].copy_(
dones.to(buffer_device), non_blocking=True
)
terminateds = (
terminateds
if terminateds is not None
else torch.zeros_like(dones, dtype=torch.bool)
)
truncateds = (
truncateds
if truncateds is not None
else torch.zeros_like(dones, dtype=torch.bool)
)
self.rollout_buffer["terminated"][
: self.num_envs, self.current_rollout_step
].copy_(terminateds.to(buffer_device), non_blocking=True)
self.rollout_buffer["truncated"][
: self.num_envs, self.current_rollout_step
].copy_(truncateds.to(buffer_device), non_blocking=True)
def _normalize_demo_action(
self, action: EnvAction | ControllerAction
) -> EnvAction | ControllerAction:
"""Normalize one raw action or preserve a controller-ready envelope."""
if isinstance(action, ControllerAction):
return action.snapshot()
expected_dim = int(np.prod(self.single_action_space.shape))
return self._normalize_demo_action_tensor(action, expected_dim)
def _mask_demo_action(
self,
action: EnvAction | ControllerAction,
active_mask: Sequence[bool],
) -> EnvAction | ControllerAction:
"""Accept an asynchronously completed vector-demo action.
Raw actions may still require :class:`ActionManager` preprocessing, so
the actual hold/no-op substitution is applied to the controller command
in :meth:`_preprocess_action`. Subclasses with a specialized safe action
may override this hook and return a replacement raw action.
Args:
action: Raw normalized action for the shared demo step.
active_mask: Rows that still participate in the episode.
Returns:
The raw action to preprocess.
"""
self._set_demo_active_mask(active_mask)
return action
def _prepare_controller_action(self, action: EnvAction) -> EnvAction:
"""Validate and normalize one command at the robot-control boundary.
Both raw actions emitted by ``ActionManager`` and explicit
:class:`ControllerAction` values pass through this method exactly once.
Controller tensors must use a supported command key, match the vector
environment batch, and target either active joints or the full robot.
Args:
action: Candidate controller command.
Returns:
A controller command whose tensors are on the environment device.
Raises:
TypeError: If the action or one of its commands has an invalid type.
ValueError: If its keys, batch, dimensions, or values are invalid.
"""
supported_keys = ("qpos", "qvel", "qf")
active_dim = len(self.active_joint_ids)
full_dim = int(self.robot.get_qpos().shape[-1])
def prepare_command(command: torch.Tensor, key: str) -> torch.Tensor:
if not isinstance(command, torch.Tensor):
raise TypeError(f"Controller {key} command must be a torch.Tensor.")
if not command.is_floating_point():
raise TypeError(f"Controller {key} command must be floating point.")
if command.dim() != 2 or command.shape[0] != self.num_envs:
raise ValueError(
f"Controller {key} command must have shape (num_envs, D); "
f"received {tuple(command.shape)} for {self.num_envs} environments."
)
if command.shape[1] not in {active_dim, full_dim}:
raise ValueError(
f"Controller {key} command has dim {command.shape[1]}; expected "
f"active-joint dim {active_dim} or full robot dim {full_dim}."
)
return command.to(self.device)
if isinstance(action, torch.Tensor):
return prepare_command(action, "qpos")
if not isinstance(action, TensorDict):
raise TypeError(
"Controller action must be a torch.Tensor or TensorDict, "
f"got {type(action).__name__}."
)
if tuple(action.batch_size) != (self.num_envs,):
raise ValueError(
"Controller TensorDict batch size must match num_envs; "
f"received {tuple(action.batch_size)} for {self.num_envs} environments."
)
keys = tuple(action.keys())
control_keys = tuple(key for key in supported_keys if key in action)
if not control_keys:
raise ValueError(
"Controller TensorDict must contain at least one of "
f"{list(supported_keys)}; received keys {list(keys)}."
)
prepared = action.clone().to(self.device)
for key in control_keys:
prepared[key] = prepare_command(prepared[key], key)
return prepared
def _mask_controller_demo_action(self, action: EnvAction) -> EnvAction:
"""Replace inactive controller commands with a safe hold or no-op."""
active_mask = self._demo_active_mask
if bool(active_mask.all()):
return action
def replace_rows(
value: torch.Tensor, replacement: torch.Tensor, key: str
) -> torch.Tensor:
if value.ndim < 2 or value.shape[0] != self.num_envs:
raise ValueError(
f"Cannot mask demo {key} action with shape {tuple(value.shape)}; "
f"expected a leading vector-env dimension of {self.num_envs}."
)
masked = value.clone()
inactive = ~active_mask.to(value.device)
masked[inactive] = replacement.to(device=value.device, dtype=value.dtype)[
inactive
]
return masked
measured_qpos = self.robot.get_qpos()
active_qpos = measured_qpos[:, self.active_joint_ids]
def qpos_replacement(value: torch.Tensor) -> torch.Tensor:
"""Select the measured qpos layout matching the controller command."""
if value.shape[1:] == active_qpos.shape[1:]:
return active_qpos
if value.shape[1:] == measured_qpos.shape[1:]:
return measured_qpos
raise ValueError(
"Cannot construct a qpos hold command for controller demo action "
f"shape {tuple(value.shape)}; measured active/full layouts are "
f"{tuple(active_qpos.shape)} and {tuple(measured_qpos.shape)}."
)
if isinstance(action, torch.Tensor):
return replace_rows(action, qpos_replacement(action), "qpos")
if not isinstance(action, TensorDict):
raise TypeError(
"Controller demo actions must be torch.Tensor or TensorDict, "
f"got {type(action).__name__}."
)
masked_action = action.clone()
supported_key = False
for key in ("qpos", "qvel", "qf"):
if key not in masked_action:
continue
supported_key = True
value = masked_action[key]
replacement = (
qpos_replacement(value) if key == "qpos" else torch.zeros_like(value)
)
masked_action[key] = replace_rows(value, replacement, key)
if not supported_key:
raise ValueError(
"Cannot mask a controller demo TensorDict without qpos, qvel, or qf."
)
return masked_action
def _normalize_demo_action_list(
self, action_list: Sequence[EnvAction] | torch.Tensor | None
) -> Sequence[EnvAction] | torch.Tensor | None:
"""Validate/convert demo action outputs to match single action-space dim."""
if action_list is None:
return None
# Use the per-env action space, not the (batched) ``action_space`` whose
# shape is ``(num_envs, dim)``. Otherwise demo actions shaped
# ``(num_envs, dim)`` are rejected with "action dim < expected" for
# ``num_envs > 1`` (expected would be ``num_envs * dim``).
expected_dim = int(np.prod(self.single_action_space.shape))
if isinstance(action_list, torch.Tensor):
return self._normalize_demo_action_tensor(action_list, expected_dim)
if not isinstance(action_list, Sequence):
raise TypeError(
"create_demo_action_list must return None, a torch.Tensor, or a sequence of actions. "
f"Got {type(action_list)}."
)
normalized_action_list = [
self._normalize_demo_action(action) for action in action_list
]
return type(action_list)(normalized_action_list)
def _normalize_demo_action_tensor(
self, action: EnvAction | torch.Tensor, expected_dim: int
) -> EnvAction | torch.Tensor:
"""Normalize one action tensor to the expected action dimension.
Conversion rule:
- If last-dim equals action-space dim, keep as-is.
- If last-dim is larger, slice with ``active_joint_ids``.
- If last-dim is smaller, raise ``ValueError``.
"""
if isinstance(action, TensorDict):
return self._normalize_demo_action_tensordict(action, expected_dim)
if not isinstance(action, torch.Tensor):
raise TypeError(
"Each demo action must be a torch.Tensor or TensorDict. "
f"Got {type(action)}."
)
if action.ndim == 0:
raise ValueError(
"Demo action tensor must have at least one dimension with action features on the last axis."
)
action_dim = int(action.shape[-1])
if action_dim == expected_dim:
return action
if action_dim < expected_dim:
raise ValueError(
"Demo action dim is smaller than action space dim and cannot be auto-converted. "
f"Got action dim={action_dim}, expected={expected_dim}."
)
return self._slice_action_with_active_joint_ids(
action, action_dim, expected_dim
)
def _normalize_demo_action_tensordict(
self, action: TensorDict, expected_dim: int
) -> TensorDict:
"""Normalize tensor entries in a TensorDict action payload."""
converted_action = action.clone()
for key in ("qpos", "qvel", "qf"):
if key not in converted_action:
continue
value = converted_action[key]
if value.ndim == 0:
raise ValueError(
f"Demo action TensorDict['{key}'] must have at least one dimension."
)
action_dim = int(value.shape[-1])
if action_dim == expected_dim:
continue
if action_dim < expected_dim:
raise ValueError(
f"Demo action TensorDict['{key}'] dim={action_dim} is smaller than expected action dim={expected_dim}."
)
converted_action[key] = self._slice_action_with_active_joint_ids(
value, action_dim, expected_dim
)
return converted_action
def _slice_action_with_active_joint_ids(
self, action: torch.Tensor, action_dim: int, expected_dim: int
) -> torch.Tensor:
"""Slice a high-dimensional action to active joints.
This is used when demo actions are generated in full-DoF form while the
environment action-space only controls active joints.
"""
if len(self.active_joint_ids) != expected_dim:
raise ValueError(
"Cannot convert demo action by active_joint_ids because their length does not match the action space dim. "
f"len(active_joint_ids)={len(self.active_joint_ids)}, expected={expected_dim}."
)
if len(self.active_joint_ids) == 0:
raise ValueError(
"Cannot convert demo action by active_joint_ids because active_joint_ids is empty."
)
return action[..., self.active_joint_ids]
def _step_action(self, action: EnvAction) -> EnvAction:
"""Set action control command into simulation.
Supports multiple action formats:
1. torch.Tensor: Interpreted as qpos (joint positions)
2. Dict with keys:
- "qpos": Joint positions
- "qvel": Joint velocities
- "qf": Joint forces/torques
Args:
action: The action applied to the robot agent.
Returns:
The action return.
"""
def active_joint_command(command: torch.Tensor, key: str) -> torch.Tensor:
"""Normalize full-robot commands to the active-joint layout."""
active_dim = len(self.active_joint_ids)
if command.shape[-1] == active_dim:
return command.to(self.device)
full_dim = int(self.robot.get_qpos().shape[-1])
if command.shape[-1] == full_dim:
return command[..., self.active_joint_ids].to(self.device)
raise ValueError(
f"Controller {key} action has dim {command.shape[-1]}; expected "
f"active-joint dim {active_dim} or full robot dim {full_dim}."
)
if isinstance(action, TensorDict):
# Support multiple control modes simultaneously
action = action.clone()
if "qpos" in action:
action["qpos"] = active_joint_command(action["qpos"], "qpos")
self.robot.set_qpos(
qpos=action["qpos"], joint_ids=self.active_joint_ids
)
if "qvel" in action:
action["qvel"] = active_joint_command(action["qvel"], "qvel")
self.robot.set_qvel(
qvel=action["qvel"], joint_ids=self.active_joint_ids
)
if "qf" in action:
action["qf"] = active_joint_command(action["qf"], "qf")
self.robot.set_qf(qf=action["qf"], joint_ids=self.active_joint_ids)
elif isinstance(action, torch.Tensor):
action = active_joint_command(action, "qpos")
self.robot.set_qpos(qpos=action, joint_ids=self.active_joint_ids)
else:
logger.log_error(f"Unsupported action type: {type(action)}")
return action
[docs]
def compute_task_state(
self, **kwargs
) -> Tuple[torch.Tensor, torch.Tensor, Dict[str, Any]]:
"""Compute task-specific state: success, fail, and metrics.
Override this method in subclass to define task-specific logic for RL tasks.
Returns:
Tuple of (success, fail, metrics):
- success: Boolean tensor of shape (num_envs,)
- fail: Boolean tensor of shape (num_envs,)
- metrics: Dict of metric tensors
"""
success = torch.zeros(self.num_envs, device=self.device, dtype=torch.bool)
fail = torch.zeros(self.num_envs, device=self.device, dtype=torch.bool)
metrics: Dict[str, Any] = {}
return success, fail, metrics
[docs]
def get_info(self, **kwargs) -> Dict[str, Any]:
"""Get environment info dictionary.
Calls compute_task_state() to get task-specific success/fail/metrics when
available. Subclasses should override compute_task_state() for RL tasks.
Returns:
Info dictionary with success, fail, elapsed_steps, metrics
"""
success, fail, metrics = self.compute_task_state(**kwargs)
info: Dict[str, Any] = {
"success": success,
"fail": fail,
"elapsed_steps": self._elapsed_steps,
"metrics": metrics,
}
return info
[docs]
def evaluate(self, **kwargs) -> Dict[str, Any]:
"""Evaluate the environment state.
Returns:
Evaluation dictionary with success and metrics
"""
info = self.get_info(**kwargs)
eval_dict: Dict[str, Any] = {
"success": info["success"][0].item(),
}
if "metrics" in info:
for key, value in info["metrics"].items():
eval_dict[key] = value
return eval_dict
def _preprocess_action(self, action: EnvAction | ControllerAction) -> EnvAction:
"""Resolve one raw or controller-ready action for robot control."""
is_controller_action = isinstance(action, ControllerAction)
if is_controller_action:
action = action.value
if self._traj_buffer is not None:
self._traj_raw_action = (
action.clone() if hasattr(action, "clone") else action
)
if self.action_manager is not None and not is_controller_action:
action = self.action_manager.process_action(action, mode="pre")
elif not is_controller_action:
action = super()._preprocess_action(action)
action = self._prepare_controller_action(action)
if getattr(self, "_demo_no_auto_reset", False):
action = self._mask_controller_demo_action(action)
return action
def _postprocess_action(self, action):
if self.action_manager is not None:
return self.action_manager.process_action(action, mode="post")
return super()._postprocess_action(action)
def _setup_robot(self, **kwargs) -> Robot:
"""Setup the robot in the environment.
Currently, only joint position control is supported. Would be extended to support joint velocity and torque
control in the future.
Returns:
Robot: The robot instance added to the scene.
"""
if self.cfg.robot is None:
logger.log_error("Robot configuration is not provided.")
# Initialize the robot based on the configuration.
robot: Robot = self.sim.add_robot(self.cfg.robot)
# Setup active joints for robot to control.
if self.cfg.control_parts:
if len(self.cfg.active_joint_ids) > 0:
logger.log_error(
f"Both control_parts and active_joint_ids are specified in the configuration. Please specify only one of them."
)
# Check env control parts are valid
for part_name in self.cfg.control_parts:
if part_name not in robot.control_parts:
logger.log_error(
f"Invalid control part: {part_name}. The supported control parts are: {robot.control_parts}"
)
for part_name in self.cfg.control_parts:
self.active_joint_ids.extend(
robot.get_joint_ids(name=part_name, remove_mimic=True)
)
elif self.cfg.active_joint_ids:
# Check env active joint ids are valid
for joint_id in self.cfg.active_joint_ids:
if joint_id not in robot.active_joint_ids:
logger.log_error(
f"Invalid active joint id: {joint_id}. The supported active joint ids are: {robot.active_joint_ids}"
)
self.active_joint_ids = self.cfg.active_joint_ids
else:
# Use all joints of the robot.
self.active_joint_ids = list(range(robot.dof))
robot.build_pk_serial_chain()
qpos_limits = (
robot.body_data.qpos_limits[0, self.active_joint_ids].cpu().numpy()
)
self.single_action_space = gym.spaces.Box(
low=qpos_limits[:, 0], high=qpos_limits[:, 1], dtype=np.float32
)
return robot
def _setup_sensors(self, **kwargs) -> Dict[str, BaseSensor]:
"""Setup the sensors in the environment.
Returns:
Dict[str, BaseSensor]: A dictionary mapping sensor UIDs to sensor instances.
"""
# TODO: support sensor attachment to the robot.
sensors = {}
for cfg in self.cfg.sensor:
sensor = self.sim.add_sensor(cfg)
sensors[cfg.uid] = sensor
return sensors
def _setup_lights(self) -> None:
"""Setup the lights in the environment."""
# Set direct lights.
for cfg in self.cfg.light.direct:
self.sim.add_light(cfg=cfg)
# Set indirect lights.
if self.cfg.light.indirect is not None:
if "emission_light" in self.cfg.light.indirect:
self.sim.set_emission_light(**self.cfg.light.indirect["emission_light"])
if "env_map" in self.cfg.light.indirect:
path = get_data_path(self.cfg.light.indirect["env_map"])
self.sim.set_indirect_lighting(path)
def _setup_background(self) -> None:
"""Setup the static rigid objects in the environment."""
for cfg in self.cfg.background:
if cfg.body_type == "dynamic":
logger.log_error(
f"Background object must be kinematic or static rigid object."
)
self.sim.add_rigid_object(cfg=cfg)
def _setup_interactive_objects(self) -> None:
"""Setup the interactive objects in the environment."""
for cfg in self.cfg.articulation:
self.sim.add_articulation(cfg=cfg)
for cfg in self.cfg.rigid_object:
if cfg.body_type != "dynamic":
logger.log_error(
f"Interactive rigid object must be dynamic rigid object."
)
self.sim.add_rigid_object(cfg=cfg)
for cfg in self.cfg.rigid_object_group:
self.sim.add_rigid_object_group(cfg=cfg)
[docs]
def preview_sensor_data(
self,
name: str,
data_type: str = "color",
env_ids: int = 0,
method: str = "cv2",
save: bool = False,
) -> None:
"""Preview the sensor data by matplotlib
Note:
Currently only support RGB image preview.
Args:
name (str): name of the sensor to preview.
data_type (str): type of the sensor data to preview.
env_ids (int): index of the arena to preview. Defaults to 0.
method (str): method to preview the sensor data. Currently support "plt" and "cv2". Defaults to "cv2".
save (bool): whether to save the preview image. Defaults to False.
"""
# TODO: this function need to be improved to support more sensor types and data types.
sensor = self.get_sensor(name=name)
if data_type not in sensor.SUPPORTED_DATA_TYPES:
logger.log_error(
f"Data type '{data_type}' not supported by sensor '{name}'. Supported types: {sensor.SUPPORTED_DATA_TYPES}"
)
sensor.update()
data = sensor.get_data()
# TODO: maybe put the preview (visualization) method to the sensor class.
if sensor.cfg.sensor_type == "StereoCamera":
view = data[data_type][env_ids].cpu().numpy()
view_right = data[f"{data_type}_right"][env_ids].cpu().numpy()
view = np.concatenate((view, view_right), axis=1)
else:
view = data[data_type][env_ids].cpu().numpy()
if method == "cv2":
import cv2
if save:
cv2.imwrite(
f"sensor_data_{data_type}.png",
cv2.cvtColor(view, cv2.COLOR_RGB2BGR),
)
else:
window_name = f"sensor_data_{data_type}"
height, width = view.shape[:2]
cv2.namedWindow(window_name, cv2.WINDOW_NORMAL)
cv2.resizeWindow(window_name, width, height)
cv2.imshow(window_name, cv2.cvtColor(view, cv2.COLOR_RGB2BGR))
cv2.waitKey(0)
cv2.destroyWindow(window_name)
elif method == "plt":
from matplotlib import pyplot as plt
plt.imshow(view)
if save:
plt.savefig(f"sensor_data_{data_type}.png")
plt.close()
else:
plt.show()
[docs]
def create_demo_action_list(self, *args, **kwargs) -> Sequence[EnvAction] | None:
"""Create a demonstration action list for the environment.
This function should be implemented in subclasses to generate a sequence of actions
that demonstrate a specific task or behavior within the environment.
Returns:
Sequence[EnvAction] | None: A list of actions if a demonstration is available, otherwise None.
Note:
Subclass outputs are automatically post-processed by the base class:
action last-dimension must match ``single_action_space``. If larger,
actions are sliced by ``active_joint_ids``; if smaller, ``ValueError``
is raised.
"""
raise NotImplementedError(
"The method 'create_demo_action_list' must be implemented in subclasses."
)
@property
def task_program_adapter(self) -> TaskProgramEnvironmentAdapter:
"""Return the adapter injected after the environment built its scene.
A registered environment normally receives an
:class:`TaskProgramAdapterFactory` through its :class:`EnvSpec`.
Advanced integrations may still override this property.
"""
adapter = self._task_program_adapter
if adapter is None:
raise NotImplementedError(
"An environment with an Task Program must receive an "
"task_program_adapter_factory or override "
"task_program_adapter."
)
return adapter
def _checked_task_program_adapter(self) -> TaskProgramEnvironmentAdapter:
"""Return the exact configured adapter before touching live providers."""
from embodichain.lab.task_program.integrations import (
TaskProgramEnvironmentAdapter,
)
adapter = self.task_program_adapter
if type(adapter) is not TaskProgramEnvironmentAdapter:
raise TypeError(
"task_program_adapter must be exactly " "TaskProgramEnvironmentAdapter."
)
return adapter
[docs]
def compile_task_program(
self,
program: TaskProgramCfg,
) -> CompiledTaskProgram:
"""Compile a configured Task Program through the explicit adapter.
Args:
program: Strict Task Program configuration attached to ``cfg``.
Returns:
Provider-free compiled program ready for runtime assembly.
"""
return self._checked_task_program_adapter().compile(program)
[docs]
def create_task_program_bridge(
self,
program: CompiledTaskProgram,
) -> TaskProgramDemoBridge:
"""Create the Gym demo bridge through the explicit adapter.
Args:
program: Compiled provider-free Task Program.
Returns:
Atomic demo bridge whose segments are consumed lazily.
"""
return self._checked_task_program_adapter().create_bridge(program)
[docs]
def is_task_success(self, **kwargs: Any) -> torch.Tensor:
"""Return completed Task Program acceptance or legacy task success.
Task Program success is published only after its bridge has consumed
every segment lifecycle, including post-policies and validators.
Args:
**kwargs: Compatibility keywords forwarded for tasks without a Task Program.
Returns:
Per-environment task-success mask.
"""
bridge = self._active_task_program_bridge
task_program = getattr(getattr(self, "cfg", None), "task_program", None)
if bridge is None:
if task_program is None:
return super().is_task_success(**kwargs)
return torch.zeros(
self.num_envs,
dtype=torch.bool,
device=self.device,
)
if not bridge.program_completed:
return torch.zeros(
self.num_envs,
dtype=torch.bool,
device=self.device,
)
completion = bridge.completion_mask
if completion.dtype != torch.bool or completion.shape != (self.num_envs,):
raise ValueError(
"Task Program completion mask must be bool with one value per "
"environment."
)
return completion.to(device=self.device)
[docs]
def create_demo_segments(
self,
*args,
task_program: TaskProgramCfg | CompiledTaskProgram | None = None,
**kwargs,
) -> Iterable[DemoSegment] | None:
"""Create the semantic segments that make up one task episode.
An episode-level ``task_program`` takes precedence over the static
configuration. This lets trusted callers supply a model-produced,
already compiled program without mutating :attr:`cfg`. Otherwise, a
configured program is compiled through the injected adapter. With no
selected program, the legacy action-list path remains unchanged.
Args:
*args: Positional arguments forwarded to the legacy planner.
task_program: Optional episode-level program config or provider-free
compiled program.
**kwargs: Keyword arguments forwarded to the legacy planner.
Returns:
Segment sequence, or ``None`` when planning fails.
"""
selected_program = task_program
if selected_program is None:
selected_program = getattr(
getattr(self, "cfg", None),
"task_program",
None,
)
if selected_program is not None:
from embodichain.lab.task_program import CompiledTaskProgram
compiled_program = (
selected_program
if type(selected_program) is CompiledTaskProgram
else self.compile_task_program(selected_program)
)
bridge = self.create_task_program_bridge(compiled_program)
self._active_task_program_bridge = bridge
return bridge.iter_segments()
actions = self.create_demo_action_list(*args, **kwargs)
if actions is None:
return None
return (DemoSegment(actions=actions, name="legacy"),)
[docs]
def save_trajectory(self, path: str, env_ids: Sequence[int] | None = None) -> str:
"""Save a causally aligned trajectory to a ``.pt`` file.
``states[t]`` is the state immediately before ``actions[t]`` is applied,
matching the frame alignment used by expert/LeRobot trajectories.
Args:
path: Destination ``.pt`` file path.
env_ids: Env indices to save (default: all). Each saved env's actual
recorded length is stored in ``meta["lengths"]``.
Raises:
RuntimeError: If trajectory recording was never enabled.
"""
if self._traj_buffer is None:
raise RuntimeError(
"Trajectory recording is not enabled (set cfg.record_trajectory=True)."
)
if env_ids is None:
env_ids = list(range(self.num_envs))
env_ids = list(env_ids)
if not env_ids:
raise ValueError("env_ids must contain at least one environment row.")
env_ids_t = torch.as_tensor(env_ids, dtype=torch.long, device=self.device)
lengths = self._traj_steps[env_ids_t]
max_len = int(lengths.max().item()) if len(env_ids) > 0 else 0
sub = self._traj_buffer[env_ids_t]
states = sub["states"][:, :max_len].clone()
actions = sub["actions"][:, :max_len].clone()
meta = {
"lengths": lengths.tolist(),
"num_steps": max_len,
"num_envs": int(len(env_ids)),
# ``dt`` historically represented trajectory timing. Keep the key
# for compatibility, but make it describe the actual interval
# between recorded environment steps rather than a physics substep.
"dt": self.step_dt,
"physics_dt": self.physics_dt,
"sim_steps_per_control": int(self.cfg.sim_steps_per_control),
"step_dt": self.step_dt,
"control_frequency": self.control_frequency,
"active_joint_ids": list(self.active_joint_ids),
"robot_uid": self.robot.uid,
"robot_dof": int(self.robot.dof),
"articulation_uids": list(self.sim._articulations.keys()),
"articulation_dofs": {
uid: int(art.dof) for uid, art in self.sim._articulations.items()
},
"rigid_object_uids": list(self.sim._rigid_objects.keys()),
"env_ids": [int(e) for e in env_ids],
"demo_episodes": [
self.get_demo_episode_metadata(int(env_id)) for env_id in env_ids
],
}
torch.save({"states": states, "actions": actions, "meta": meta}, path)
return path
def _save_trajectory_for_env(self, env_id: int) -> str | None:
"""Persist one explicitly committed environment trajectory.
I/O errors are intentionally propagated: reset is the commit boundary,
and clearing the in-memory trajectory after a failed write would report
success while losing committed data.
"""
if self._traj_buffer is None or not self.cfg.trajectory_auto_save:
return None
if int(self._traj_steps[env_id].item()) == 0:
return None
base = self.cfg.trajectory_save_dir
if base is None:
base = os.path.join(
EMBODICHAIN_DEFAULT_DATA_ROOT, "trajectories", self._traj_run_id
)
os.makedirs(base, exist_ok=True)
path = os.path.join(base, f"traj_env{env_id}_{self._traj_save_count:06d}.pt")
saved_path = self.save_trajectory(path, env_ids=[env_id])
self._traj_save_count += 1
return saved_path
def _discard_pending_recordings(self) -> None:
"""Abort recorder state that has not crossed an explicit reset commit."""
errors: list[str] = []
if self.cfg.events and self.event_manager is not None:
from embodichain.lab.gym.envs.managers.record import record_camera_data
for mode_cfgs in self.event_manager._mode_functor_cfgs.values():
for functor_cfg in mode_cfgs:
if isinstance(functor_cfg.func, record_camera_data):
recorder = functor_cfg.func
recorder_name = type(recorder).__name__
try:
recorder.discard_and_clear()
except Exception as error:
errors.append(f"{recorder_name} discard: {error}")
try:
recorder.finalize()
except Exception as error:
errors.append(f"{recorder_name} finalize: {error}")
if self._traj_steps is not None:
try:
self._traj_steps.zero_()
except Exception as error:
errors.append(f"trajectory discard: {error}")
if self.rollout_buffer is not None and self._rollout_buffer_mode != "rl":
try:
env_ids = torch.arange(self.num_envs, device=self.device)
self._clear_expert_rollout_rows(env_ids)
self.rollout_steps.zero_()
self.current_rollout_step = 0
except Exception as error:
errors.append(f"expert rollout discard: {error}")
if errors:
raise RuntimeError(
f"Failed to abort {len(errors)} pending recorder operation(s): "
+ "; ".join(errors)
)
[docs]
def close(self, *, exit_process: bool | None = None) -> None:
"""Abort pending data, finalize committed writes, and release resources.
Closing is idempotent and is never an implicit episode commit. A demo
episode enters the dataset only through ``reset(save_data=True)``;
partial data left by failure, cancellation, or interpreter shutdown is
discarded before recorder finalization.
Args:
exit_process: Forwarded to :meth:`SimulationManager.destroy` after
successful cleanup. Error paths always disable process exit so
the durability exception can propagate.
Raises:
RuntimeError: If one or more recorders fail their durability barrier.
"""
close_lock = getattr(self, "_close_lock", None)
if close_lock is None:
close_lock = threading.RLock()
self._close_lock = close_lock
with close_lock:
if getattr(self, "_closed", False):
close_error = getattr(self, "_close_error", None)
if close_error is not None:
raise close_error
return
errors: list[Exception] = []
try:
self._discard_pending_recordings()
except Exception as error:
errors.append(error)
if self.dataset_manager:
try:
self.dataset_manager.finalize()
except Exception as error:
errors.append(error)
# Report before sim.destroy(): the default destroy path exits the
# process, so diagnostics and durability checks must finish first.
try:
self._profiler.report()
except Exception as error:
errors.append(error)
if errors:
# Queue simulator cleanup without hiding persistence failures
# behind SimulationManager's default os._exit(0).
try:
self.sim.destroy(exit_process=False)
except Exception as error:
errors.append(error)
messages = "; ".join(str(error) for error in errors)
close_error = RuntimeError(
f"Failed to close EmbodiedEnv cleanly: {messages}"
)
self._close_error = close_error
self._closed = True
raise close_error from errors[0]
try:
if exit_process is None:
self.sim.destroy()
else:
self.sim.destroy(exit_process=exit_process)
except Exception as error:
self._close_error = error
self._closed = True
raise
self._closed = True