Create a solver

Overview

The solver module in EmbodiChain provides a unified and extensible interface for robot kinematics computation, including forward kinematics (FK), inverse kinematics (IK), and Jacobian calculation. It supports multiple solver backends (e.g., Pinocchio, OPW, SRS, PINK, PyTorch) and is designed for both simulation and real-robot applications.

Key Features

• Unified API: Abstract base class (BaseSolver) defines a common interface for all solvers.

• Multiple Backends: Supports Pinocchio, OPW, SRS, PINK, PyTorch, and differential solvers.

• Flexible Configuration: Easily switch solver type and parameters via configuration.

• Batch and Single Query: Supports both batch and single FK/IK/Jacobian queries.

• Extensible: New solvers can be added by subclassing BaseSolver and implementing required methods.

The Code

The tutorial corresponds to the srs_solver.py script in the scripts/tutorials/sim directory.

Code for srs_solver.py

# ----------------------------------------------------------------------------
# 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.
# ----------------------------------------------------------------------------

"""Move a W1 end effector along a Cartesian straight line with SRS IK."""

from __future__ import annotations

import argparse
import sys
import time
import traceback

import numpy as np
import torch

from embodichain.lab.sim import SimulationManager, SimulationManagerCfg
from embodichain.lab.sim.cfg import MarkerCfg
from embodichain.lab.sim.objects import Robot
from embodichain_robots import DexforceW1Cfg
from embodichain.lab.visualization import (
    VisualizationCfg,
    add_viser_args_to_parser,
    visualization_cfg_from_args,
)


def main(
    device: str,
    num_steps: int,
    line_offset: tuple[float, float, float],
    physics_steps: int,
    max_joint_step_deg: float,
    visualization: VisualizationCfg | None = None,
) -> None:
    """Run sequential SRS IK targets along a fixed-orientation straight line.

    Args:
        device: Simulation and SRS solver device, either ``cpu`` or ``cuda``.
        num_steps: Number of Cartesian waypoints, including both endpoints.
        line_offset: End-point translation relative to the initial TCP pose, in meters.
        physics_steps: Number of simulation steps displayed per waypoint.
        max_joint_step_deg: Maximum allowed change of any joint per IK waypoint.
        visualization: Optional visualization configuration.
    """
    if num_steps < 2:
        raise ValueError("num_steps must be at least 2")
    if physics_steps < 1:
        raise ValueError("physics_steps must be at least 1")
    if max_joint_step_deg <= 0.0:
        raise ValueError("max_joint_step_deg must be positive")
    if device == "cuda" and not torch.cuda.is_available():
        raise RuntimeError(
            "CUDA was requested, but PyTorch cannot access a CUDA device"
        )

    np.set_printoptions(precision=5, suppress=True)
    torch.set_printoptions(precision=5, sci_mode=False)
    sim = SimulationManager(
        SimulationManagerCfg(
            # Keep the native window closed while planning so renderer/window
            # lifecycle events cannot terminate or perturb timed CUDA IK calls.
            headless=True,
            device=device,
            width=2200,
            height=1200,
            visualization=visualization or VisualizationCfg(),
        )
    )
    # Keep physics paused during pose conversion, IK, and FK error measurement.

    try:
        arm_name = "left_arm"
        robot_cfg = DexforceW1Cfg.from_dict({"uid": "dexforce_w1"})
        # The robot default intentionally ignores the elbow joint when ranking IK
        # candidates. For a Cartesian-path tutorial, weight every joint to prevent
        # visually disruptive branch changes between adjacent waypoints.
        robot_cfg.solver_cfg[arm_name].ik_nearest_weight = np.array(
            [2.0, 2.0, 2.0, 2.0, 1.0, 1.0, 1.0]
        )
        robot: Robot = sim.add_robot(cfg=robot_cfg)
        sim.prepare()
        joint_ids = robot.get_joint_ids(arm_name)
        qpos_seed = torch.tensor(
            [[np.pi / 6, 0.0, 0.0, -np.pi / 2, 0.0, 0.0, np.pi / 6]],
            dtype=torch.float32,
            device=sim.device,
        )
        reference_qpos = qpos_seed.clone()
        robot.set_qpos(qpos_seed, joint_ids=joint_ids, target=False)
        robot.set_qpos(qpos_seed, joint_ids=joint_ids, target=True)
        sim.update(step=physics_steps)

        start_pose = robot.compute_fk(qpos=qpos_seed, name=arm_name, to_matrix=True)
        target_poses = start_pose.repeat(num_steps, 1, 1)
        offset = torch.tensor(
            line_offset, dtype=start_pose.dtype, device=start_pose.device
        )
        interpolation = torch.linspace(
            0.0, 1.0, num_steps, dtype=start_pose.dtype, device=start_pose.device
        )
        target_poses[:, :3, 3] += interpolation.unsqueeze(1) * offset

        # Warm up lazy FK compilation and the selected SRS backend outside timing.
        robot.compute_ik(
            pose=target_poses[:1],
            joint_seed=qpos_seed,
            name=arm_name,
            return_all_solutions=True,
        )
        if device == "cuda":
            torch.cuda.synchronize()
            torch.cuda.reset_peak_memory_stats()

        solve_times_ms: list[float] = []
        candidate_counts: list[int] = []
        translation_errors_mm: list[float] = []
        waypoint_candidates: list[torch.Tensor] = []
        continuity_weights = torch.tensor(
            [2.0, 2.0, 2.0, 2.0, 1.0, 1.0, 1.0],
            dtype=qpos_seed.dtype,
            device=qpos_seed.device,
        )
        max_joint_step = torch.deg2rad(
            torch.tensor(
                max_joint_step_deg, dtype=qpos_seed.dtype, device=qpos_seed.device
            )
        )
        for waypoint, target_pose in enumerate(target_poses):
            start_time = time.perf_counter()
            success, solution = robot.compute_ik(
                pose=target_pose.unsqueeze(0),
                # Use one fixed seed while enumerating candidates. Path continuity
                # is selected globally below instead of greedily changing the
                # candidate set after every waypoint.
                joint_seed=reference_qpos,
                name=arm_name,
                return_all_solutions=True,
            )
            if device == "cuda":
                torch.cuda.synchronize()
            solve_times_ms.append((time.perf_counter() - start_time) * 1000.0)

            if not bool(success[0]):
                raise RuntimeError(
                    f"Trajectory planning failed at waypoint "
                    f"{waypoint + 1}/{num_steps}; nothing was executed."
                )

            candidates = solution[0]
            candidate_counts.append(candidates.shape[0])
            waypoint_candidates.append(candidates)
            print(
                f"Enumerated {waypoint + 1:03d}/{num_steps}: "
                f"solve={solve_times_ms[-1]:.3f} ms, "
                f"candidates={candidates.shape[0]}"
            )

        # Find a minimum-cost path through the layered IK candidate graph. This
        # avoids the greedy failure mode where a locally attractive arm angle has
        # no continuous successor at a later waypoint.
        path_costs: list[torch.Tensor] = []
        predecessors: list[torch.Tensor] = []
        first_delta = torch.atan2(
            torch.sin(waypoint_candidates[0] - reference_qpos),
            torch.cos(waypoint_candidates[0] - reference_qpos),
        )
        first_allowed = first_delta.abs().amax(dim=1) <= max_joint_step
        first_cost = (first_delta.square() * continuity_weights).sum(dim=1)
        first_cost.masked_fill_(~first_allowed, float("inf"))
        path_costs.append(first_cost)
        predecessors.append(torch.full_like(first_cost, -1, dtype=torch.long))

        for waypoint in range(1, num_steps):
            previous_candidates = waypoint_candidates[waypoint - 1]
            candidates = waypoint_candidates[waypoint]
            edge_delta = torch.atan2(
                torch.sin(candidates[:, None, :] - previous_candidates[None, :, :]),
                torch.cos(candidates[:, None, :] - previous_candidates[None, :, :]),
            )
            allowed_edges = edge_delta.abs().amax(dim=2) <= max_joint_step
            transition_cost = (edge_delta.square() * continuity_weights).sum(dim=2)
            transition_cost.masked_fill_(~allowed_edges, float("inf"))
            reference_delta = torch.atan2(
                torch.sin(candidates - reference_qpos),
                torch.cos(candidates - reference_qpos),
            )
            node_cost = 0.05 * (reference_delta.square() * continuity_weights).sum(
                dim=1
            )
            total_cost = transition_cost + path_costs[-1].unsqueeze(0)
            best_cost, best_predecessor = total_cost.min(dim=1)
            best_cost += node_cost
            if not bool(torch.isfinite(best_cost).any()):
                reachable_previous = torch.isfinite(path_costs[-1])
                reachable_edges = edge_delta[:, reachable_previous]
                smallest_step = reachable_edges.abs().amax(dim=2).min()
                raise RuntimeError(
                    f"No globally continuous IK path reaches waypoint "
                    f"{waypoint + 1}/{num_steps}: smallest available maximum "
                    f"joint step is {torch.rad2deg(smallest_step).item():.3f} deg, "
                    f"limit is {max_joint_step_deg:.3f} deg."
                )
            path_costs.append(best_cost)
            predecessors.append(best_predecessor)

        selected_indices = [int(path_costs[-1].argmin())]
        for waypoint in range(num_steps - 1, 0, -1):
            selected_indices.append(int(predecessors[waypoint][selected_indices[-1]]))
        selected_indices.reverse()
        planned_qpos = [
            waypoint_candidates[i][selected_indices[i]].unsqueeze(0)
            for i in range(num_steps)
        ]

        for waypoint, (target_pose, qpos_seed) in enumerate(
            zip(target_poses, planned_qpos, strict=True)
        ):
            actual_pose = robot.compute_fk(
                qpos=qpos_seed, name=arm_name, to_matrix=True
            )
            error_mm = float(
                torch.linalg.vector_norm(
                    actual_pose[0, :3, 3] - target_pose[:3, 3]
                ).item()
                * 1000.0
            )
            translation_errors_mm.append(error_mm)
            print(
                f"Planned {waypoint + 1:03d}/{num_steps}: " f"error={error_mm:.4f} mm"
            )

        print(
            f"SRS {device.upper()} planning summary: {num_steps}/{num_steps} solved, "
            f"median/p95/mean solve={np.median(solve_times_ms):.3f}/"
            f"{np.percentile(solve_times_ms, 95):.3f}/"
            f"{np.mean(solve_times_ms):.3f} ms, "
            f"max translation error="
            f"{max(translation_errors_mm, default=float('nan')):.4f} mm"
        )
        print(
            f"IK candidates per waypoint min/median/max: "
            f"{min(candidate_counts)}/{int(np.median(candidate_counts))}/"
            f"{max(candidate_counts)}"
        )
        if device == "cuda":
            print(
                f"CUDA peak allocated memory: "
                f"{torch.cuda.max_memory_allocated() / 1024**2:.2f} MiB"
            )
        planned_qpos_tensor = torch.cat(planned_qpos)
        wrapped_steps = torch.atan2(
            torch.sin(planned_qpos_tensor[1:] - planned_qpos_tensor[:-1]),
            torch.cos(planned_qpos_tensor[1:] - planned_qpos_tensor[:-1]),
        )
        max_step_flat_index = wrapped_steps.abs().argmax()
        max_step_waypoint = int(max_step_flat_index // wrapped_steps.shape[1]) + 2
        max_step_joint = int(max_step_flat_index % wrapped_steps.shape[1]) + 1
        print(
            f"Max adjacent joint step: "
            f"{torch.rad2deg(wrapped_steps.abs().max()).item():.3f} deg "
            f"at waypoint {max_step_waypoint}, joint {max_step_joint}"
        )
        if wrapped_steps.shape[0] > 1:
            joint_step_changes = wrapped_steps[1:] - wrapped_steps[:-1]
            print(
                f"Max joint step change: "
                f"{torch.rad2deg(joint_step_changes.abs().max()).item():.3f} deg"
            )

        sim.open_window()
        marker_stride = max(1, num_steps // 25)
        marker_indices = torch.arange(0, num_steps, marker_stride, device=sim.device)
        if marker_indices[-1] != num_steps - 1:
            marker_indices = torch.cat(
                (marker_indices, marker_indices.new_tensor([num_steps - 1]))
            )
        sim.draw_marker(
            MarkerCfg(
                name="srs_target_path",
                marker_type="axis",
                axis_xpos=target_poses[marker_indices],
                axis_size=0.0006,
                axis_len=0.008,
                arena_index=0,
            )
        )

        print("Planning completed; executing the joint trajectory...")
        execution_errors_mm: list[float] = []
        previous_qpos = robot.get_qpos(name=arm_name).clone()
        zero_qvel = torch.zeros_like(previous_qpos)
        for waypoint, qpos in enumerate(planned_qpos):
            wrapped_delta = torch.atan2(
                torch.sin(qpos - previous_qpos), torch.cos(qpos - previous_qpos)
            )
            for substep in range(1, physics_steps + 1):
                alpha = substep / physics_steps
                interpolated_qpos = previous_qpos + alpha * wrapped_delta
                robot.set_qpos(interpolated_qpos, joint_ids=joint_ids, target=False)
                robot.set_qpos(interpolated_qpos, joint_ids=joint_ids, target=True)
                robot.set_qvel(zero_qvel, joint_ids=joint_ids, target=False)
                robot.set_qvel(zero_qvel, joint_ids=joint_ids, target=True)
                sim.update(step=1)
            previous_qpos = qpos
            actual_qpos = robot.get_qpos(name=arm_name)
            actual_pose = robot.compute_fk(
                qpos=actual_qpos, name=arm_name, to_matrix=True
            )
            execution_errors_mm.append(
                float(
                    torch.linalg.vector_norm(
                        actual_pose[0, :3, 3] - target_poses[waypoint, :3, 3]
                    ).item()
                    * 1000.0
                )
            )
            if waypoint % marker_stride == 0 or waypoint == num_steps - 1:
                sim.draw_marker(
                    MarkerCfg(
                        name=f"srs_executed_path_{waypoint:03d}",
                        marker_type="axis",
                        axis_xpos=actual_pose,
                        axis_size=0.0012,
                        axis_len=0.004,
                        arena_index=0,
                    )
                )
        print("Trajectory execution completed.")
        print(
            f"Max execution tracking error: {max(execution_errors_mm):.4f} mm. "
            "Long axes show targets; short thick axes show executed samples."
        )
        sim.capture_visualization(force=True)
    finally:
        # Do not use the default os._exit(0) cleanup path: it suppresses Python
        # tracebacks raised during planning and makes failures look like clean exits.
        sim.destroy(exit_process=False)


if __name__ == "__main__":
    parser = argparse.ArgumentParser(description=__doc__)
    parser.add_argument(
        "--device", choices=("cpu", "cuda"), default="cpu", help="SRS backend."
    )
    parser.add_argument(
        "--num-steps", type=int, default=50, help="Number of line waypoints."
    )
    parser.add_argument(
        "--line-offset",
        type=float,
        nargs=3,
        metavar=("X", "Y", "Z"),
        default=(0.0, -0.30, 0.0),
        help="TCP line displacement in meters (default: 0 -0.30 0).",
    )
    parser.add_argument(
        "--physics-steps",
        type=int,
        default=20,
        help="Simulation interpolation steps per IK waypoint (default: 20).",
    )
    parser.add_argument(
        "--max-joint-step-deg",
        type=float,
        default=15.0,
        help="Reject an IK branch changing any joint by more than this angle.",
    )
    add_viser_args_to_parser(parser)
    args = parser.parse_args()
    exit_code = 0
    try:
        main(
            device=args.device,
            num_steps=args.num_steps,
            line_offset=tuple(args.line_offset),
            physics_steps=args.physics_steps,
            max_joint_step_deg=args.max_joint_step_deg,
            visualization=visualization_cfg_from_args(args),
        )
    except BaseException:
        traceback.print_exc()
        exit_code = 1
    finally:
        # Deferred destruction is only safe after main() has unwound and no local
        # Robot/solver wrappers remain live on its Python frame.
        SimulationManager.flush_cleanup_queue()
    sys.exit(exit_code)

Typical Usage

Step 1: Configure solver

from embodichain.lab.sim.motion.solvers import SRSSolverCfg

srs_cfg = SRSSolverCfg(
    urdf_path="/path/to/robot.urdf",
    joint_names=[
        "shoulder_pan_joint", "shoulder_lift_joint", "elbow_joint",
        "wrist_1_joint", "wrist_2_joint", "wrist_3_joint"
    ],
    end_link_name="ee_link",
    root_link_name="base_link"
)

Step 2: Instantiate the robot with solver

import torch

robot_cfg.solver_cfg = srs_cfg
robot = Robot(cfg=robot_cfg, device=torch.device("cpu"))

Step 3: Use FK/IK/Jacobian

import numpy as np
import torch

qpos = torch.tensor([[0.0, -1.57, 1.57, 0.0, 1.57, 0.0]])
ee_pose = robot.compute_fk(qpos=qpos, name="arm", to_matrix=True)

target_pose = np.array([
    [0, -1, 0, 0.5],
    [1,  0, 0, 0.2],
    [0,  0, 1, 0.3],
    [0,  0, 0, 1.0]
])
success, qpos_sol = robot.compute_ik(
    target_pose, joint_seed=qpos, name="arm"
)
J = robot.get_solver("arm").get_jacobian(qpos)

Note

• robot.compute_fk(qpos) internally calls the bound solver’s get_fk method.

• robot.compute_ik(target_pose, joint_seed) internally calls the solver’s get_ik method.

API Reference

BaseSolver

class BaseSolver:
    def get_fk(self, qpos, **kwargs) -> torch.Tensor:
        """Compute forward kinematics for the end-effector."""

    def get_ik(self, target_pose, joint_seed=None, num_samples=None, **kwargs) -> Tuple[torch.Tensor, torch.Tensor]:
        """Compute inverse kinematics for a given pose."""

    def get_jacobian(self, qpos, locations=None, jac_type="full") -> torch.Tensor:
        """Compute the Jacobian matrix for the given joint positions."""

• set_ik_nearest_weight: Set weights for IK nearest-neighbor search.

• set_qpos_limits / get_qpos_limits: Set or get joint position limits.

• set_tcp / get_tcp: Set or get the tool center point (TCP) transformation.

Configuration

• All solvers are configured via a SolverCfg or its subclass (e.g., PinkSolverCfg).

• Key config fields: urdf_path, joint_names, end_link_name, root_link_name, tcp, and solver-specific parameters.

• Use cfg.init_solver() to instantiate the solver, or assign to robot_cfg.solver_cfg for automatic integration.

Notes & Best Practices

• Always ensure URDF and joint/link names match your robot model.

• For IK, providing a good qpos_seed improves convergence and solution quality.

• Use set_iteration_params (if available) to tune solver performance for your application.

• For custom robots or new algorithms, subclass BaseSolver and register your solver.

See Also

• Motion Generator — Motion Generator

• Creating a Basic Environment — Basic Environment Setup