Update base planner by matafela · Pull Request #173 · DexForce/EmbodiChain

matafela · 2026-03-11T08:48:05Z

Description

Update BasePlanner.

TODO:

make modification to existed examples and environments
refacotr create_discrete_trajectory with plan (we could make xpos and is_linear as constraints)

Type of change

Enhancement (non-breaking change which improves an existing functionality)

Checklist

I have run the black . command to format the code base.
I have made corresponding changes to the documentation
I have added tests that prove my fix is effective or that my feature works
Dependencies have been updated, if applicable.

Copilot

Pull request overview

This PR updates the planning API to use a structured PlanState object (instead of raw dicts) and adapts the TOPPRA planner and motion generator to the new interface.

Changes:

Introduces PlanState (plus MoveType / MovePart) in planner utils.
Refactors BasePlanner and ToppraPlanner to accept PlanState inputs and initializes planners via **kwargs.
Updates MotionGenerator to construct PlanState objects before invoking the planner.

Reviewed changes

Copilot reviewed 4 out of 4 changed files in this pull request and generated 8 comments.

File	Description
`embodichain/lab/sim/planners/utils.py`	Adds `PlanState` and related enums used to represent planning inputs more explicitly.
`embodichain/lab/sim/planners/base_planner.py`	Updates base planner API to accept `PlanState` and switches init to `**kwargs`.
`embodichain/lab/sim/planners/toppra_planner.py`	Updates TOPPRA planner to accept `PlanState` and adjusts waypoint handling accordingly.
`embodichain/lab/sim/planners/motion_generator.py`	Packs dict-based states into `PlanState` before calling the underlying planner.

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Copilot · 2026-03-11T08:52:34Z

embodichain/lab/sim/planners/utils.py

+    move_part: MovePart = MovePart.LEFT
+    xpos: torch.Tensor = None
+    qpos: torch.Tensor = None
+    qacc: torch.Tensor = None
+    qvel: torch.Tensor = None


PlanState fields are annotated as torch.Tensor but default to None (and are also populated with lists in MotionGenerator.plan). Consider widening these types (e.g., torch.Tensor | np.ndarray | Sequence[float] | None) and/or using Optional[...] to match the actual inputs and avoid type-checking/runtime surprises.

embodichain/lab/sim/planners/base_planner.py

embodichain/lab/sim/planners/toppra_planner.py

Copilot · 2026-03-11T08:52:36Z

embodichain/lab/sim/planners/motion_generator.py

+            qvel=current_state["velocity"],
+            qacc=current_state["acceleration"],


MotionGenerator.plan previously worked when callers provided only position (docstring says velocity/acceleration are optional), but this new packing code unconditionally indexes current_state["velocity"] and ["acceleration"], which will raise KeyError for minimal inputs. Please default missing values (e.g., zeros) or reuse _create_state_dict-style normalization before building PlanState.

Suggested change

qvel=current_state["velocity"],

qacc=current_state["acceleration"],

qvel=current_state.get("velocity", np.zeros(self.dof)),

qacc=current_state.get("acceleration", np.zeros(self.dof)),

embodichain/lab/sim/planners/toppra_planner.py

embodichain/lab/sim/planners/utils.py

Copilot

Pull request overview

Copilot reviewed 4 out of 4 changed files in this pull request and generated 5 comments.

Comments suppressed due to low confidence (1)

embodichain/lab/sim/planners/toppra_planner.py:63

plan() now takes PlanState, but the docstring still refers to state dictionaries. Please update the parameter documentation to match PlanState (e.g., which fields are required such as qpos).

        r"""Execute trajectory planning.

        Args:
            current_state: Dictionary containing 'position', 'velocity', 'acceleration' for current state
            target_states: List of dictionaries containing target states

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embodichain/lab/sim/planners/base_planner.py

embodichain/lab/sim/planners/toppra_planner.py

embodichain/lab/sim/planners/utils.py

Copilot · 2026-03-12T04:11:39Z

embodichain/lab/sim/planners/motion_generator.py

 import torch
 import numpy as np
 from typing import Dict, List, Tuple, Union, Any
 from enum import Enum
 from scipy.spatial.transform import Rotation, Slerp

 from embodichain.lab.sim.planners.toppra_planner import ToppraPlanner
 from embodichain.lab.sim.planners.utils import TrajectorySampleMethod
 from embodichain.lab.sim.objects.robot import Robot
 from embodichain.utils import logger
-
-
-class PlannerType(Enum):
-    r"""Enumeration for different planner types."""
-
-    TOPPRA = "toppra"
-    """TOPPRA planner for time-optimal trajectory planning."""
+from embodichain.lab.sim.planners.utils import PlanState, MoveType, MovePart



After removing PlannerType, from enum import Enum appears unused in this module. Please drop the unused import to avoid lint failures and keep the import list accurate.

embodichain/lab/sim/planners/toppra_planner.py

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Copilot reviewed 4 out of 4 changed files in this pull request and generated 6 comments.

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embodichain/lab/sim/planners/toppra_planner.py

embodichain/lab/sim/planners/motion_generator.py

Copilot · 2026-03-12T07:18:49Z

embodichain/lab/sim/planners/motion_generator.py

@@ -500,10 +473,7 @@ def calculate_point_allocations(
            logger.log_error("Failed to plan trajectory")

        # Convert positions to list
-        out_qpos_list = (
-            positions.tolist() if isinstance(positions, np.ndarray) else positions
-        )
-
+        out_qpos_list = positions.to("cpu").numpy().tolist()


out_qpos_list = positions.to("cpu").numpy().tolist() assumes positions is always a torch.Tensor. With the current ToppraPlanner.plan early-return branch, positions can be a NumPy array, causing an AttributeError here. Either enforce the torch return type in all planners (preferred) or add a safe conversion/branch here.

embodichain/lab/sim/planners/base_planner.py

embodichain/lab/sim/planners/motion_generator.py

embodichain/lab/sim/planners/utils.py

Copilot

Pull request overview

Copilot reviewed 4 out of 4 changed files in this pull request and generated 7 comments.

Comments suppressed due to low confidence (1)

embodichain/lab/sim/planners/motion_generator.py:521

This converts positions (already a torch.Tensor) to NumPy and back to a new CPU tensor, which is unnecessary and can change dtype (often to float64). Prefer keeping it as a tensor (e.g., positions.detach().cpu().to(torch.float32)) and, if you need a list, call .tolist() once at the boundary.

        out_qpos_list = positions.to("cpu").numpy().tolist()
        out_qpos_list = (
            torch.tensor(out_qpos_list)
            if not isinstance(out_qpos_list, torch.Tensor)
            else out_qpos_list
        )

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embodichain/lab/sim/planners/base_planner.py

embodichain/lab/sim/planners/toppra_planner.py

embodichain/lab/sim/planners/base_planner.py

embodichain/lab/sim/planners/toppra_planner.py

embodichain/lab/sim/planners/motion_generator.py

 from embodichain.lab.sim.planners.utils import TrajectorySampleMethod
 from embodichain.lab.sim.objects.robot import Robot
 from embodichain.utils import logger
+from embodichain.lab.sim.planners.utils import PlanState, MoveType, MovePart


embodichain/lab/sim/planners/base_planner.py

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embodichain/lab/sim/planners/motion_generator.py

@@ -24,13 +24,48 @@
 from embodichain.lab.sim.planners.utils import TrajectorySampleMethod
 from embodichain.lab.sim.objects.robot import Robot
 from embodichain.utils import logger
+from dataclasses import dataclass


embodichain/lab/sim/planners/motion_generator.py

embodichain/lab/sim/planners/utils.py

embodichain/lab/sim/planners/toppra_planner.py

embodichain/lab/sim/planners/base_planner.py

embodichain/lab/sim/planners/toppra_planner.py

Copilot

Pull request overview

Copilot reviewed 16 out of 16 changed files in this pull request and generated 11 comments.

Comments suppressed due to low confidence (1)

docs/source/tutorial/motion_gen.rst:71

create_discrete_trajectory signature has changed (it now takes tensors and a runtime cfg, and sample_num is no longer a parameter). This example call with sample_num=20 will not work; update it to construct a runtime cfg (e.g., ToppraPlannerRuntimeCfg(sample_method=..., sample_interval=...)) and pass it via cfg=....

   # Generate a discrete trajectory (joint or Cartesian)
   qpos_list, xpos_list = motion_gen.create_discrete_trajectory(
       qpos_list=[[0,0,0,0,0,0],[0.5,0.2,0,0,0,0]],
       sample_num=20
   )

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embodichain/lab/sim/planners/base_planner.py

docs/source/overview/sim/planners/toppra_planner.md

- `times` is the array of time stamps.
- `duration` is the total trajectory time.
+- `result.positions`, `result.velocities`, `result.accelerations` are arrays of sampled trajectory points.
+- `result.dt` is the array of time stamps.


embodichain/lab/sim/planners/base_planner.py

+            empty_qpos = torch.empty((0, self.dof), dtype=torch.float32)
+            empty_xpos = torch.empty((0, 4, 4), dtype=torch.float32)
+            return empty_qpos, empty_xpos


embodichain/lab/sim/planners/base_planner.py

embodichain/lab/sim/planners/toppra_planner.py

        # Create TOPPRA-specific constraint arrays (symmetric format)
        # This format is required by TOPPRA library
-        self.vlims = np.array([[-v, v] for v in max_constraints["velocity"]])
-        self.alims = np.array([[-a, a] for a in max_constraints["acceleration"]])
+        if isinstance(cfg.constraints["velocity"], float):
+            self.vlims = np.array(
+                [
+                    [-cfg.constraints["velocity"], cfg.constraints["velocity"]]
+                    for _ in range(self.dofs)
+                ]
+            )
+        else:
+            self.vlims = np.array(cfg.constraints["velocity"])
+
+        if isinstance(cfg.constraints["acceleration"], float):
+            self.alims = np.array(
+                [
+                    [-cfg.constraints["acceleration"], cfg.constraints["acceleration"]]
+                    for _ in range(self.dofs)
+                ]
+            )
+        else:
+            self.alims = np.array(cfg.constraints["acceleration"])


docs/source/overview/sim/planners/toppra_planner.md

        "velocity": [1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
        "acceleration": [2.0, 2.0, 2.0, 2.0, 2.0, 2.0]


embodichain/lab/sim/planners/motion_generator.py

+            return empty_qpos, empty_xpos

        # Plan trajectory using internal plan method
-        success, positions, velocities, accelerations, times, duration = self.plan(
-            current_state=current_state,
-            target_states=target_states,
-            sample_method=sample_method,
-            sample_interval=sample_num,
-            **kwargs,
+        plan_result = self.plan(
+            current_state=init_plan_state, target_states=target_plan_states, cfg=cfg
        )

-        if not success or positions is None:
+        if not plan_result.success or plan_result.positions is None:


tests/sim/planners/test_toppra_planner.py

+
+        result = self.planner.plan(current_state, target_states, sample_interval=0.1)
+        assert result.success is True


embodichain/lab/sim/planners/base_planner.py

+        # Input validation
+        if len(xpos_list) < 2:
+            logger.log_warning("xpos_list must contain at least 2 points")
+            empty_qpos = torch.empty((0, self.dof), dtype=torch.float32)
+            empty_xpos = torch.empty((0, 4, 4), dtype=torch.float32)
+            return empty_qpos, empty_xpos
+
+        # Calculate point allocations for interpolation
+        interpolated_point_allocations = calculate_point_allocations(
+            xpos_list, step_size=0.002, angle_step=np.pi / 90
+        )
+


embodichain/lab/sim/planners/base_planner.py

+            move_type=MoveType.JOINT_MOVE, qpos=interpolate_qpos_list[0]
+        )
+        target_states = []
+        for qpos in interpolate_qpos_list:


embodichain/lab/sim/planners/base_planner.py

embodichain/lab/sim/planners/toppra_planner.py

yuecideng · 2026-03-17T10:17:44Z

embodichain/lab/sim/planners/base_planner.py

+    qpos_seed: torch.Tensor | None = None
+    """Optional seed joint configuration for IK initialization during interpolation. Should be a 1D tensor of shape (DOF,). If None, the first point in qpos_list will be used as the seed."""
+
+    is_linear: bool = True


Move is_linear and interpolate_step to MotionGenerator

Copilot

Pull request overview

Copilot reviewed 16 out of 16 changed files in this pull request and generated 17 comments.

Comments suppressed due to low confidence (2)

docs/source/tutorial/motion_gen.rst:72

This section still documents create_discrete_trajectory(...), but that method was removed from MotionGenerator in this PR. Either update the docs to describe the replacement workflow (building PlanState + runtime_cfg and calling plan()), or keep create_discrete_trajectory as a compatibility wrapper.

   # Generate a discrete trajectory (joint or Cartesian)
   qpos_list, xpos_list = motion_gen.create_discrete_trajectory(
       qpos_list=[[0,0,0,0,0,0],[0.5,0.2,0,0,0,0]],
       sample_num=20
   )

docs/source/overview/sim/planners/motion_generator.md:116

This doc still demonstrates motion_gen.plan(current_state=..., sample_method=..., sample_interval=...) and create_discrete_trajectory(...), but MotionGenerator.plan no longer takes current_state and create_discrete_trajectory was removed. Update the documentation to reflect the new API (pass runtime_cfg and PlanState waypoints), otherwise the examples won’t run.

### Trajectory Planning

#### Joint Space Planning

```python
from embodichain.lab.sim.planners.utils import PlanState

current_state = PlanState(qpos=[0, 0, 0, 0, 0, 0])
target_states = [
    PlanState(qpos=[1, 1, 1, 1, 1, 1])
]
result = motion_gen.plan(
    current_state=current_state,
    target_states=target_states,
    sample_method=TrajectorySampleMethod.TIME,
    sample_interval=0.01
)

Cartesian or Joint Interpolation

# Using joint configurations (qpos_list)
qpos_list = [
    [0, 0, 0, 0, 0, 0],
    [1, 1, 1, 1, 1, 1]
]
out_qpos_list, out_xpos_list = motion_gen.create_discrete_trajectory(
    qpos_list=qpos_list,
    is_linear=False,
    sample_method=TrajectorySampleMethod.QUANTITY,
    sample_num=20
)

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embodichain/lab/sim/planners/base_planner.py

+        if xpos_list is None:
+            logger.log_warning("Either xpos_list or qpos_list must be provided")
+            empty_qpos = torch.empty((0, self.dof), dtype=torch.float32)
+            empty_xpos = torch.empty((0, 4, 4), dtype=torch.float32)
+            return empty_qpos, empty_xpos


embodichain/lab/sim/planners/motion_generator.py

+            current_state: PlanState
+            target_states: List of PlanState
+            cfg:  Planner runtime configuration.


embodichain/lab/sim/planners/base_planner.py

+            for i in range(self.dofs):
+                label = f"Joint {i+1}" if b == 0 else ""
+                axs[0].plot(


embodichain/lab/sim/planners/motion_generator.py

+            init_plan_state, target_plan_states = self.planner.interpolate_trajectory(
+                control_part=runtime_cfg.control_part,
+                xpos_list=xpos_list,
+                qpos_list=qpos_list,
+                cfg=runtime_cfg,
            )
-
-            # Check if current position is significantly different from first waypoint
-            pos_diff = np.linalg.norm(current_xpos[:3, 3] - xpos_list[0][:3, 3])
-            rot_diff = np.linalg.norm(current_xpos[:3, :3] - xpos_list[0][:3, :3])
-
-            if pos_diff > 0.001 or rot_diff > 0.01:
-                xpos_list = np.concatenate(
-                    [current_xpos[None, :, :], xpos_list], axis=0
-                )
-                if qpos_list is not None:
-                    qpos_list = np.concatenate(
-                        [current_qpos[None, :], qpos_list], axis=0
-                    )
-
-        if qpos_seed is None and qpos_list is not None:
-            qpos_seed = qpos_list[0]
-
-        # Input validation
-        if len(xpos_list) < 2:
-            logger.log_warning("xpos_list must contain at least 2 points")
-            return [], []
-
-        # Calculate point allocations for interpolation
-        interpolated_point_allocations = calculate_point_allocations(
-            xpos_list, step_size=0.002, angle_step=np.pi / 90
-        )
-
-        # Generate trajectory
-        interpolate_qpos_list = []
-        if is_linear or qpos_list is None:
-            # Linear cartesian interpolation
-            for i in range(len(xpos_list) - 1):
-                interpolated_poses = interpolate_xpos(
-                    xpos_list[i], xpos_list[i + 1], interpolated_point_allocations[i]
-                )
-                for xpos in interpolated_poses:
-                    success, qpos = self.robot.compute_ik(
-                        pose=xpos, joint_seed=qpos_seed, name=self.uid
-                    )
-
-                    if isinstance(success, torch.Tensor):
-                        is_success = bool(success.all())
-                    elif isinstance(success, np.ndarray):
-                        is_success = bool(np.all(success))
-                    elif isinstance(success, (list, tuple)):
-                        is_success = all(success)
-                    else:
-                        is_success = bool(success)
-
-                    if isinstance(qpos, torch.Tensor):
-                        has_nan = torch.isnan(qpos).any().item()
-                    else:
-                        has_nan = np.isnan(qpos).any()
-
-                    if not is_success or qpos is None or has_nan:
-                        logger.log_debug(
-                            f"IK failed or returned nan at pose, skipping this point."
-                        )
-                        continue
-
-                    interpolate_qpos_list.append(
-                        qpos[0] if isinstance(qpos, (np.ndarray, list)) else qpos
-                    )
-                    qpos_seed = (
-                        qpos[0] if isinstance(qpos, (np.ndarray, list)) else qpos
-                    )
        else:


docs/source/tutorial/motion_gen.rst

+   from embodichain.lab.sim.planners import MotionGenerator, MotionGenCfg, ToppraPlannerCfg
+
+   # Assume you have a robot instance and arm_name
+   motion_cfg = MotionGenCfg(
+       planner_cfg=ToppraPlannerCfg(
+           robot_uid=robot.uid,
+           control_part=arm_name,
+           constraints={
+               "velocity": 0.2,
+               "acceleration": 0.5,
+           },
+       )


tests/sim/planners/test_toppra_planner.py

+        assert self.planner.dofs == 6
+        assert len(self.planner.vlims) == 6
+        assert len(self.planner.alims) == 6
+        assert self.planner.device == torch.device("cpu")
+


tests/sim/planners/test_toppra_planner.py

+    def test_plan_basic(self):
+        current_state = PlanState(qpos=np.zeros(6))
+        target_states = [PlanState(qpos=np.ones(6))]
+
+        result = self.planner.plan(current_state, target_states, sample_interval=0.1)
+        assert result.success is True


docs/source/tutorial/motion_gen.rst

   # Plan a joint-space trajectory
-   current_state = {"position": [0, 0, 0, 0, 0, 0]}
-   target_states = [{"position": [0.5, 0.2, 0, 0, 0, 0]}]
-   success, positions, velocities, accelerations, times, duration = motion_gen.plan(
+   current_state = PlanState(position=np.array([0., 0., 0., 0., 0., 0.]))
+   target_states = [PlanState(position=np.array([0.5, 0.2, 0., 0., 0., 0.]))]
+   plan_result = motion_gen.plan(
       current_state=current_state,
       target_states=target_states
   )
+   success = plan_result.success
+   positions = plan_result.positions
+   velocities = plan_result.velocities
+   accelerations = plan_result.accelerations
+   times = plan_result.t_series
+   duration = plan_result.duration



docs/source/overview/sim/planners/toppra_planner.md

+from embodichain.lab.sim.planners.utils import TrajectorySampleMethod, PlanState
 from embodichain.lab.sim.planners.toppra_planner import ToppraPlanner
-success, positions, velocities, accelerations, times, duration = planner.plan(
-    current_state={
-        "position": [0, 0, 0, 0, 0, 0],
-        "velocity": [0, 0, 0, 0, 0, 0],
-        "acceleration": [0, 0, 0, 0, 0, 0]
-    },
+result = planner.plan(
+    current_state=PlanState(qpos=[0, 0, 0, 0, 0, 0]),
    target_states=[
-        {"position": [1, 1, 1, 1, 1, 1]}
+        PlanState(qpos=[1, 1, 1, 1, 1, 1])
    ],
    sample_method=TrajectorySampleMethod.TIME,
    sample_interval=0.01
 )
 ```

- `positions`, `velocities`, `accelerations` are arrays of sampled trajectory points.
- `times` is the array of time stamps.
- `duration` is the total trajectory time.
+- `result.positions`, `result.velocities`, `result.accelerations` are arrays of sampled trajectory points.
+- `result.dt` is the array of time stamps.
+- `result.duration` is the total trajectory time.


embodichain/lab/sim/planners/base_planner.py

+        if self.dofs > 0 and has_constraints:
+            plot_idx = 1
+            if vels is not None:
+                max_vel = self.cfg.constraints["velocity"][0]
+                axs[plot_idx].plot(
+                    time_steps,
+                    [-max_vel] * len(time_steps),
+                    "k--",
+                    label="Max Velocity",
+                )
+                axs[plot_idx].plot(time_steps, [max_vel] * len(time_steps), "k--")
+                plot_idx += 1
+
+            if accs is not None:
+                max_acc = self.cfg.constraints["acceleration"][0]
+                axs[plot_idx].plot(


…EmbodiChain into cj/update-base-planner

Copilot

Pull request overview

Copilot reviewed 17 out of 17 changed files in this pull request and generated 16 comments.

Comments suppressed due to low confidence (2)

docs/source/overview/sim/planners/motion_generator.md:116

The MotionGenerator doc still imports ToppraPlannerCfg from embodichain.lab.sim.planners.motion_generator, but that class is defined in toppra_planner (or re-exported from embodichain.lab.sim.planners). Also, the examples below still use the removed current_state argument and create_discrete_trajectory API. Please update imports and usage to the new plan(target_states, runtime_cfg=...) flow.

from embodichain.lab.sim.planners.motion_generator import MotionGenerator, MotionGenCfg, ToppraPlannerCfg
from embodichain.lab.sim.objects.robot import Robot
from embodichain.lab.sim.solvers.pink_solver import PinkSolverCfg
from embodichain.lab.sim.planners.utils import TrajectorySampleMethod

# Configure the simulation
sim_cfg = SimulationManagerCfg(
    width=1920,
    height=1080,
    physics_dt=1.0 / 100.0,
    sim_device="cpu",
)

sim = SimulationManager(sim_cfg)

# Get UR10 URDF path
urdf_path = get_data_path("UniversalRobots/UR10/UR10.urdf")

# Create UR10 robot
robot_cfg = RobotCfg(
    uid="UR10_test",
    urdf_cfg=URDFCfg(
        components=[{"component_type": "arm", "urdf_path": urdf_path}]
    ),
    control_parts={"arm": ["Joint[1-6]"]},
    solver_cfg={
        "arm": PinkSolverCfg(
            urdf_path=urdf_path,
            end_link_name="ee_link",
            root_link_name="base_link",
            pos_eps=1e-2,
            rot_eps=5e-2,
            max_iterations=300,
            dt=0.1,
        )
    },
    drive_pros=JointDrivePropertiesCfg(
        stiffness={"Joint[1-6]": 1e4},
        damping={"Joint[1-6]": 1e3},
    ),
)
robot = sim.add_robot(cfg=robot_cfg)

motion_gen = MotionGenerator(
    cfg=MotionGenCfg(
        planner_cfg=ToppraPlannerCfg(
            robot_uid="UR10_test",
            control_part="arm",
            constraints={
                "velocity": 0.2,
                "acceleration": 0.5,
            },
        )
    )
)

Trajectory Planning

Joint Space Planning

from embodichain.lab.sim.planners.utils import PlanState

current_state = PlanState(qpos=[0, 0, 0, 0, 0, 0])
target_states = [
    PlanState(qpos=[1, 1, 1, 1, 1, 1])
]
result = motion_gen.plan(
    current_state=current_state,
    target_states=target_states,
    sample_method=TrajectorySampleMethod.TIME,
    sample_interval=0.01
)

Cartesian or Joint Interpolation

# Using joint configurations (qpos_list)
qpos_list = [
    [0, 0, 0, 0, 0, 0],
    [1, 1, 1, 1, 1, 1]
]
out_qpos_list, out_xpos_list = motion_gen.create_discrete_trajectory(
    qpos_list=qpos_list,
    is_linear=False,
    sample_method=TrajectorySampleMethod.QUANTITY,
    sample_num=20
)

docs/source/tutorial/motion_gen.rst:72

This section still documents the old MotionGenerator.plan(current_state=..., sample_method=..., sample_interval=...) and create_discrete_trajectory(...) APIs, and references plan_result.t_series (which doesn’t exist; PlanResult currently exposes dt). Since these APIs were removed/changed in this PR, the tutorial should be updated to the new plan(target_states, runtime_cfg=...) signature (and either remove create_discrete_trajectory usage or replace it with is_pre_interpolate=True).

   # Plan a joint-space trajectory
   current_state = PlanState(position=np.array([0., 0., 0., 0., 0., 0.]))
   target_states = [PlanState(position=np.array([0.5, 0.2, 0., 0., 0., 0.]))]
   plan_result = motion_gen.plan(
       current_state=current_state,
       target_states=target_states
   )
   success = plan_result.success
   positions = plan_result.positions
   velocities = plan_result.velocities
   accelerations = plan_result.accelerations
   times = plan_result.t_series
   duration = plan_result.duration

   # Generate a discrete trajectory (joint or Cartesian)
   qpos_list, xpos_list = motion_gen.create_discrete_trajectory(
       qpos_list=[[0,0,0,0,0,0],[0.5,0.2,0,0,0,0]],
       sample_num=20
   )

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embodichain/lab/sim/planners/base_planner.py

+
+        if xpos_list is None:
+            logger.log_warning("Either xpos_list or qpos_list must be provided")
+            empty_qpos = torch.empty((0, self.dof), dtype=torch.float32)


embodichain/lab/sim/planners/base_planner.py

+    def interpolate_trajectory(
+        self,
+        control_part: str | None = None,
+        xpos_list: torch.Tensor | None = None,
+        qpos_list: torch.Tensor | None = None,
+        cfg: BasePlannerRuntimeCfg = BasePlannerRuntimeCfg(),
+    ) -> tuple[PlanState, list[PlanState]]:
+        if qpos_list is not None:
+            if not isinstance(qpos_list, torch.Tensor):
+                qpos_list = np.asarray(qpos_list)
+            qpos_tensor = torch.as_tensor(
+                qpos_list, dtype=torch.float32, device=self.robot.device
+            )
+            if qpos_tensor.dim() == 1:
+                qpos_tensor = qpos_tensor.unsqueeze(0)
+
+            qpos_batch = qpos_tensor.unsqueeze(0)  # [n_env=1, n_batch=N, dof]
+            xpos_batch = self.robot.compute_batch_fk(
+                qpos=qpos_batch,
+                name=control_part,
+                to_matrix=True,
+            )
+            xpos_list = xpos_batch.squeeze(0)
+            qpos_list = qpos_tensor
+
+        if xpos_list is None:
+            logger.log_warning("Either xpos_list or qpos_list must be provided")
+            empty_qpos = torch.empty((0, self.dof), dtype=torch.float32)
+            empty_xpos = torch.empty((0, 4, 4), dtype=torch.float32)
+            return empty_qpos, empty_xpos
+
+        if not isinstance(xpos_list, torch.Tensor):
+            xpos_list = torch.as_tensor(
+                np.asarray(xpos_list),
+                dtype=torch.float32,
+                device=self.robot.device,
+            )
+        else:
+            xpos_list = xpos_list.to(dtype=torch.float32, device=self.robot.device)
+
+        if cfg.start_qpos is not None:
+            start_xpos = self.robot.compute_fk(
+                qpos=cfg.start_qpos.unsqueeze(0), name=control_part, to_matrix=True
+            )
+            qpos_list = (
+                torch.cat([cfg.start_qpos.unsqueeze(0), qpos_list], dim=0)
+                if qpos_list is not None
+                else None
+            )
+            xpos_list = torch.cat([start_xpos, xpos_list], dim=0)
+        # Input validation
+        if len(xpos_list) < 2:
+            logger.log_warning("xpos_list must contain at least 2 points")
+            return None, None
+


docs/source/tutorial/motion_gen.rst

+   from embodichain.lab.sim.planners import MotionGenerator, MotionGenCfg, ToppraPlannerCfg
+
+   # Assume you have a robot instance and arm_name
+   motion_cfg = MotionGenCfg(
+       planner_cfg=ToppraPlannerCfg(
+           robot_uid=robot.uid,
+           control_part=arm_name,
+           constraints={
+               "velocity": 0.2,
+               "acceleration": 0.5,
+           },
+       )
   )
+   motion_gen = MotionGenerator(cfg=motion_cfg)

   # Plan a joint-space trajectory
-   current_state = {"position": [0, 0, 0, 0, 0, 0]}
-   target_states = [{"position": [0.5, 0.2, 0, 0, 0, 0]}]
-   success, positions, velocities, accelerations, times, duration = motion_gen.plan(
+   current_state = PlanState(position=np.array([0., 0., 0., 0., 0., 0.]))
+   target_states = [PlanState(position=np.array([0.5, 0.2, 0., 0., 0., 0.]))]
+   plan_result = motion_gen.plan(


embodichain/lab/sim/planners/base_planner.py

+            xpos_list, step_size=0.002, angle_step=np.pi / 90, device=self.device
+        )
+        # Input validation
+        if len(xpos_list) < 2:
+            logger.log_warning("xpos_list must contain at least 2 points")
+            empty_qpos = torch.empty((0, self.dof), dtype=torch.float32)
+            empty_xpos = torch.empty((0, 4, 4), dtype=torch.float32)
+            return empty_qpos, empty_xpos
+
+        # Calculate point allocations for interpolation
+        interpolated_point_allocations = calculate_point_allocations(
+            xpos_list, step_size=0.002, angle_step=np.pi / 90
+        )
+


embodichain/lab/sim/planners/base_planner.py

+        if self.dofs > 0 and has_constraints:
+            plot_idx = 1
+            if vels is not None:
+                max_vel = self.cfg.constraints["velocity"][0]
+                axs[plot_idx].plot(
+                    time_steps,
+                    [-max_vel] * len(time_steps),
+                    "k--",
+                    label="Max Velocity",
+                )
+                axs[plot_idx].plot(time_steps, [max_vel] * len(time_steps), "k--")
+                plot_idx += 1
+
+            if accs is not None:
+                max_acc = self.cfg.constraints["acceleration"][0]
+                axs[plot_idx].plot(


embodichain/lab/sim/planners/toppra_planner.py

        # Check waypoints
-        if len(current_state["position"]) != self.dofs:
-            logger.log_info("Current wayponit does not align")
-            return False, None, None, None, None, None
+        start_qpos = (
+            runtime_cfg.start_qpos
+            if runtime_cfg.start_qpos is not None
+            else target_states[0].qpos
+        )
+        if len(start_qpos) != dofs:
+            logger.log_error("Current waypoint does not align")
        for target in target_states:
-            if len(target["position"]) != self.dofs:
-                logger.log_info("Target Wayponits does not align")
-                return False, None, None, None, None, None
+            if len(target.qpos) != dofs:
+                logger.log_error("Target waypoints do not align")

        if (
            len(target_states) == 1
-            and np.sum(
-                np.abs(
-                    np.array(target_states[0]["position"])
-                    - np.array(current_state["position"])
-                )
-            )
+            and np.sum(np.abs(np.array(target_states[0].qpos) - np.array(start_qpos)))
            < 1e-3
        ):
-            logger.log_info("Only two same waypoints, do not plan")
-            return (
-                True,
-                np.array([current_state["position"], target_states[0]["position"]]),
-                np.array([[0.0] * self.dofs, [0.0] * self.dofs]),
-                np.array([[0.0] * self.dofs, [0.0] * self.dofs]),
-                0,
-                0,
+            logger.log_warning("Only two same waypoints, returning trivial trajectory.")
+            return PlanResult(
+                success=True,
+                positions=torch.as_tensor(
+                    np.array([start_qpos, target_states[0].qpos]),
+                    dtype=torch.float32,
+                    device=self.device,
+                ),
+                velocities=torch.as_tensor(
+                    np.array([[0.0] * dofs, [0.0] * dofs]),
+                    dtype=torch.float32,
+                    device=self.device,
+                ),
+                accelerations=torch.as_tensor(
+                    np.array([[0.0] * dofs, [0.0] * dofs]),
+                    dtype=torch.float32,
+                    device=self.device,
+                ),
+                dt=torch.as_tensor([0.0, 0.0], dtype=torch.float32, device=self.device),
+                duration=0.0,
            )

        # Build waypoints
-        waypoints = [np.array(current_state["position"])]
+        waypoints = [np.array(start_qpos)]
        for target in target_states:
-            waypoints.append(np.array(target["position"]))
+            waypoints.append(np.array(target.qpos))
        waypoints = np.array(waypoints)


embodichain/lab/sim/planners/motion_generator.py

@@ -51,555 +61,225 @@ class MotionGenerator:
        **kwargs: Additional arguments passed to planner initialization
    """


tests/sim/planners/test_motion_generator.py

            estimated_num = self.motion_gen.estimate_trajectory_sample_count(
-                qpos_list=self.qpos_list,
+                qpos_list=torch.as_tensor(np.array(self.qpos_list)),
                step_size=0.01,
                angle_step=np.pi / 90,
+                control_part=self.arm_name,
            )


docs/source/overview/sim/planners/toppra_planner.md

 ```python
-from embodichain.lab.sim.planners.utils import TrajectorySampleMethod
+from embodichain.lab.sim.planners.utils import TrajectorySampleMethod, PlanState
 from embodichain.lab.sim.planners.toppra_planner import ToppraPlanner
-success, positions, velocities, accelerations, times, duration = planner.plan(
-    current_state={
-        "position": [0, 0, 0, 0, 0, 0],
-        "velocity": [0, 0, 0, 0, 0, 0],
-        "acceleration": [0, 0, 0, 0, 0, 0]
-    },
+result = planner.plan(
+    current_state=PlanState(qpos=[0, 0, 0, 0, 0, 0]),
    target_states=[
-        {"position": [1, 1, 1, 1, 1, 1]}
+        PlanState(qpos=[1, 1, 1, 1, 1, 1])
    ],
    sample_method=TrajectorySampleMethod.TIME,
    sample_interval=0.01
 )
 ```

- `positions`, `velocities`, `accelerations` are arrays of sampled trajectory points.
- `times` is the array of time stamps.
- `duration` is the total trajectory time.
+- `result.positions`, `result.velocities`, `result.accelerations` are arrays of sampled trajectory points.
+- `result.dt` is the array of time stamps.
+- `result.duration` is the total trajectory time.


embodichain/lab/sim/planners/base_planner.py

+        # Check bounds
+        vel_check = torch.all(torch.abs(vels) <= max_vel).item()
+        acc_check = torch.all(torch.abs(accs) <= max_acc).item()



…ci/refactor-planner

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Pull request overview

Copilot reviewed 20 out of 20 changed files in this pull request and generated 18 comments.

Comments suppressed due to low confidence (1)

docs/source/overview/sim/planners/motion_generator.md:116

The MotionGenerator overview still references removed/changed API (control_part/constraints inside ToppraPlannerCfg, motion_gen.plan(...), and create_discrete_trajectory(...)). Update the doc to use the new MotionGenerator.generate(...) flow with MotionGenOptions + ToppraPlanOptions and the updated PlanState fields.

from embodichain.lab.sim.planners.motion_generator import MotionGenerator, MotionGenCfg, ToppraPlannerCfg
from embodichain.lab.sim.objects.robot import Robot
from embodichain.lab.sim.solvers.pink_solver import PinkSolverCfg
from embodichain.lab.sim.planners.utils import TrajectorySampleMethod

# Configure the simulation
sim_cfg = SimulationManagerCfg(
    width=1920,
    height=1080,
    physics_dt=1.0 / 100.0,
    sim_device="cpu",
)

sim = SimulationManager(sim_cfg)

# Get UR10 URDF path
urdf_path = get_data_path("UniversalRobots/UR10/UR10.urdf")

# Create UR10 robot
robot_cfg = RobotCfg(
    uid="UR10_test",
    urdf_cfg=URDFCfg(
        components=[{"component_type": "arm", "urdf_path": urdf_path}]
    ),
    control_parts={"arm": ["Joint[1-6]"]},
    solver_cfg={
        "arm": PinkSolverCfg(
            urdf_path=urdf_path,
            end_link_name="ee_link",
            root_link_name="base_link",
            pos_eps=1e-2,
            rot_eps=5e-2,
            max_iterations=300,
            dt=0.1,
        )
    },
    drive_pros=JointDrivePropertiesCfg(
        stiffness={"Joint[1-6]": 1e4},
        damping={"Joint[1-6]": 1e3},
    ),
)
robot = sim.add_robot(cfg=robot_cfg)

motion_gen = MotionGenerator(
    cfg=MotionGenCfg(
        planner_cfg=ToppraPlannerCfg(
            robot_uid="UR10_test",
            control_part="arm",
            constraints={
                "velocity": 0.2,
                "acceleration": 0.5,
            },
        )
    )
)

Trajectory Planning

Joint Space Planning

from embodichain.lab.sim.planners.utils import PlanState

current_state = PlanState(qpos=[0, 0, 0, 0, 0, 0])
target_states = [
    PlanState(qpos=[1, 1, 1, 1, 1, 1])
]
result = motion_gen.plan(
    current_state=current_state,
    target_states=target_states,
    sample_method=TrajectorySampleMethod.TIME,
    sample_interval=0.01
)

Cartesian or Joint Interpolation

# Using joint configurations (qpos_list)
qpos_list = [
    [0, 0, 0, 0, 0, 0],
    [1, 1, 1, 1, 1, 1]
]
out_qpos_list, out_xpos_list = motion_gen.create_discrete_trajectory(
    qpos_list=qpos_list,
    is_linear=False,
    sample_method=TrajectorySampleMethod.QUANTITY,
    sample_num=20
)

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embodichain/lab/sim/planners/toppra_planner.py

-                0,
-            )
+            logger.log_warning("Only two same waypoints, returning trivial trajectory.")
+            return target_states


tests/sim/planners/test_motion_generator.py

        if xpos_or_qpos == "xpos":
            estimated_num = self.motion_gen.estimate_trajectory_sample_count(
-                xpos_list=self.xpos_list,
+                xpos_list=torch.as_tensor(np.array(self.xpos_list)),
                step_size=0.01,
                angle_step=np.pi / 90,
+                control_part=self.arm_name,
            )
        else:
            estimated_num = self.motion_gen.estimate_trajectory_sample_count(
-                qpos_list=self.qpos_list,
+                qpos_list=torch.as_tensor(np.array(self.qpos_list)),
                step_size=0.01,
                angle_step=np.pi / 90,
+                control_part=self.arm_name,
            )


embodichain/lab/sim/planners/motion_generator.py

+    def generate(
+        self,
+        target_states: List[PlanState],
+        options: MotionGenOptions = MotionGenOptions(),
+    ) -> PlanResult:


embodichain/lab/sim/planners/toppra_planner.py

+    @validate_plan_options(options_cls=ToppraPlanOptions)
    def plan(
        self,
-        current_state: dict,
-        target_states: list[dict],
-        **kwargs,
-    ):
+        target_states: list[PlanState],
+        options: ToppraPlanOptions = ToppraPlanOptions(),
+    ) -> PlanResult:


embodichain/lab/sim/planners/toppra_planner.py

+        # set constraints
+        if isinstance(options.constraints["velocity"], float):
+            vlims = np.array(
+                [
+                    [
+                        -options.constraints["velocity"],
+                        options.constraints["velocity"],
+                    ]
+                    for _ in range(dofs)
+                ]
+            )
+        else:
+            vlims = np.array(options.constraints["velocity"])
+
+        if isinstance(options.constraints["acceleration"], float):
+            alims = np.array(


docs/source/tutorial/motion_gen.rst

+   from embodichain.lab.sim.planners import MotionGenerator, MotionGenCfg, ToppraPlannerCfg
+
+   # Assume you have a robot instance and arm_name
+   motion_cfg = MotionGenCfg(
+       planner_cfg=ToppraPlannerCfg(
+           robot_uid=robot.uid,
+           control_part=arm_name,
+           constraints={
+               "velocity": 0.2,
+               "acceleration": 0.5,
+           },
+       )
   )
+   motion_gen = MotionGenerator(cfg=motion_cfg)

   # Plan a joint-space trajectory
-   current_state = {"position": [0, 0, 0, 0, 0, 0]}
-   target_states = [{"position": [0.5, 0.2, 0, 0, 0, 0]}]
-   success, positions, velocities, accelerations, times, duration = motion_gen.plan(
+   current_state = PlanState(position=np.array([0., 0., 0., 0., 0., 0.]))
+   target_states = [PlanState(position=np.array([0.5, 0.2, 0., 0., 0., 0.]))]
+   plan_result = motion_gen.plan(
       current_state=current_state,
       target_states=target_states
   )
+   success = plan_result.success
+   positions = plan_result.positions
+   velocities = plan_result.velocities
+   accelerations = plan_result.accelerations
+   times = plan_result.t_series
+   duration = plan_result.duration



embodichain/lab/sim/utility/atom_action_utils.py

+    select_qpos_traj.extend(ret.positions.numpy())
+    ee_state_list_select.extend([select_arm_current_gripper_state] * len(ret.positions))




embodichain/lab/gym/envs/tasks/tableware/pour_water/action_bank.py

+            return ret.positions.numpy().T



tests/gym/envs/test_embodied_env.py

+@pytest.mark.skip(reason="Skipping CUDA tests temporarily")
 class TestCPU(EmbodiedEnvTest):
    def setup_method(self):
        self.setup_simulation("cpu", enable_rt=False)


+@pytest.mark.skip(reason="Skipping CUDA tests temporarily")


embodichain/lab/sim/planners/base_planner.py

        Note:
            - Allows 10% tolerance for velocity constraints
            - Allows 25% tolerance for acceleration constraints
            - Prints exceed information if constraints are violated
            - Assumes symmetric constraints (velocities and accelerations can be positive or negative)
+            - Supports batch dimension computation, e.g. (B, N, DOF) or (N, DOF)
        """
-        # Convert max_constraints to symmetric format for constraint checking
-        # This assumes symmetric constraints (common for most planners)
-        vlims = np.array([[-v, v] for v in self.max_constraints["velocity"]])
-        alims = np.array([[-a, a] for a in self.max_constraints["acceleration"]])
-
-        vel_check = np.all((velocities >= vlims[:, 0]) & (velocities <= vlims[:, 1]))
-        acc_check = np.all(
-            (accelerations >= alims[:, 0]) & (accelerations <= alims[:, 1])
+        device = vels.device
+
+        max_vel = torch.tensor(constraints["velocity"], dtype=vels.dtype, device=device)
+        max_acc = torch.tensor(
+            constraints["acceleration"], dtype=accs.dtype, device=device
        )

-        # 超限情况
+        # To support batching, we compute along all dimensions except the last one (DOF)
+        reduce_dims = tuple(range(vels.ndim - 1))
+
+        # Check bounds
+        vel_check = torch.all(torch.abs(vels) <= max_vel).item()
+        acc_check = torch.all(torch.abs(accs) <= max_acc).item()


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		qvel=current_state["velocity"],
		qacc=current_state["acceleration"],

		"velocity": [1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
		"acceleration": [2.0, 2.0, 2.0, 2.0, 2.0, 2.0]


		result = self.planner.plan(current_state, target_states, sample_interval=0.1)
		assert result.success is True

		@@ -51,555 +61,225 @@ class MotionGenerator:
		**kwargs: Additional arguments passed to planner initialization
		"""

		select_qpos_traj.extend(ret.positions.numpy())
		ee_state_list_select.extend([select_arm_current_gripper_state] * len(ret.positions))

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matafela commented Mar 11, 2026 • edited by yuecideng Loading Uh oh! There was an error while loading. Please reload this page.

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matafela commented Mar 11, 2026 •

edited by yuecideng

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