RoboLab Motion Planning

This checkout includes a RoboLab-native motion-planning baseline in robolab_mp/. It follows the ManiSkill example layout at a practical level: a reusable planner, task-level solution inference, a runner, recorded videos, and a verifier. It does not use SAPIEN.

What It Does

Registers requested RoboLab tasks with DroidIKActionCfg absolute Cartesian IK.
Infers simple pick/place plans from each task's success termination (object plus container, surface, or reference_object).
Executes top-down Cartesian waypoints: pregrasp, descend, close, lift, transfer, place, open, settle, retreat.
Reads simulation ground truth (world.get_pose, world.get_bbox, contact predicates) for object yaw, pose diagnostics, and final success debugging.
Records combined sensor/viewport MP4s and per-camera MP4s with RoboLab's existing VideoWriter.
Writes one motion_planning_result.json per task and a combined motion_planning_summary.json.
Optionally verifies videos are readable, have enough frames, valid dimensions, and nonblank sampled frames.

Run Three Example Tasks

From the RoboLab repo root:

cd /home/horde/robolab_mp/RoboLab
python examples/run_motion_planning.py --headless --video-mode all --verify-videos

Use the RoboLab/Isaac Python environment. In this workspace that is:

/home/horde/miniconda3/bin/conda run -n env_uwlab python examples/run_motion_planning.py \
  --headless --video-mode all --verify-videos

If Isaac Sim has not been launched in this environment before, it may prompt for the NVIDIA Omniverse EULA. Accept that prompt yourself, or run with OMNI_KIT_ACCEPT_EULA=YES only if you have already reviewed and accepted the EULA:

OMNI_KIT_ACCEPT_EULA=YES python examples/run_motion_planning.py \
  --headless --video-mode all --verify-videos

The default tasks are:

BananaInBowlTask
RubiksCubeTask
MustardInLeftBinTask

Outputs are written to:

/home/horde/robolab_mp/RoboLab/output/motion_planning/

Each task gets a directory containing MP4 files and motion_planning_result.json. The combined summary is:

output/motion_planning/motion_planning_summary.json

Run Specific Tasks

python examples/run_motion_planning.py \
  --headless \
  --task BananaInBowlTask RubiksCubeTask MustardInLeftBinTask \
  --video-mode viewport \
  --output-dir output/mp_smoke \
  --verify-videos

Useful options:

--task ...: RoboLab task class names.
--video-mode all|sensor|viewport|none: choose videos to record. all writes combined sensor/viewport videos plus individual camera videos such as *_mp_sensor_over_shoulder_left_camera.mp4, *_mp_sensor_wrist_cam.mp4, and *_mp_viewport_egocentric_mirrored_camera.mp4.
--max-steps 420: cap planner execution.
--seed 1: environment seed.
--no-save-videos: run planning without MP4 output.
--verify-videos: sanity-check generated MP4s after each task.
--record-hdf5: also export RoboLab HDF5 episode data; off by default for MP video runs.

Verify Existing Videos

python examples/verify_motion_planning_videos.py \
  --summary output/motion_planning/motion_planning_summary.json

Or verify explicit files:

python examples/verify_motion_planning_videos.py \
  output/motion_planning/*/*_mp_viewport.mp4

The verifier checks that each MP4 opens through OpenCV, contains at least eight frames, has positive dimensions, and has nonblank sampled frames.

Verified Workspace Run

The following EULA-approved headless Isaac/RoboLab run was executed in this workspace:

OMNI_KIT_ACCEPT_EULA=YES /home/horde/miniconda3/bin/conda run -n env_uwlab \
  python examples/run_motion_planning.py \
  --headless \
  --task BananaInBowlTask MustardInLeftBinTask \
  --video-mode all \
  --output-dir output/mp_yaw_camera_verified \
  --max-steps 380 \
  --verify-videos

Verified task results:

BananaInBowlTask: task_success=true, success at step 260. Final ground truth reports in_target_open_top=true, object_target_contact=true, and object_gripper_contact=false.
MustardInLeftBinTask: task_success=true, success at step 196. The first waypoint is mustard:pregrasp_yaw_0.000_offset_1.571, so the grasp command includes the object yaw from simulation plus a z-axis grasp offset.

Verified rendered MP4 outputs are under output/mp_yaw_camera_verified/. Each task directory contains:

*_mp_sensor.mp4: combined sensor strip.
*_mp_sensor_over_shoulder_left_camera.mp4: front/over-shoulder camera view.
*_mp_sensor_wrist_cam.mp4: wrist/in-hand camera view.
*_mp_viewport.mp4: combined viewport video.
*_mp_viewport_egocentric_mirrored_camera.mp4: viewport egocentric camera.

All MP4 files in this run passed the OpenCV video verifier. The summary JSON is output/mp_yaw_camera_verified/motion_planning_summary.json; each task also writes motion_planning_result.json with planner_tuning, ground_truth, waypoint targets, and actual end-effector poses.

Export GR00T Dataset

Motion-planning outputs can be converted to the GR00T LeRobot-v2 layout used by NVIDIA Isaac-GR00T:

python examples/export_motion_planning_gr00t.py \
  --input output/mp_yaw_camera_verified \
  --output output/mp_yaw_camera_verified_gr00t \
  --fps 15

The exporter writes:

meta/info.json, meta/episodes.jsonl, meta/tasks.jsonl, meta/modality.json, meta/stats.json, and meta/relative_stats.json.
data/chunk-000/episode_*.parquet with observation.state, action, timestamps, task annotations, episode indices, and terminal flags.
videos/chunk-000/observation.images.front/episode_*.mp4 for the over-shoulder/front camera.
videos/chunk-000/observation.images.wrist/episode_*.mp4 for the wrist/in-hand camera.
videos/chunk-000/observation.images.ego_view/episode_*.mp4 for the viewport egocentric camera.

The state/action vector is [x, y, z, qw, qx, qy, qz, gripper] from the MP end-effector trajectory. meta/modality.json splits this into eef_position, eef_quaternion_wxyz, and gripper, and maps videos to the GR00T keys front, wrist, and ego_view.

The verified workspace export is:

output/mp_yaw_camera_verified_gr00t/

It contains two successful episodes: Banana-in-bowl and Mustard-in-left-bin.

Extending Plans

The generic solver lives in robolab_mp/solutions.py. For standard placement tasks, it reads the success predicate params and generates a plan automatically. For harder RoboLab tasks, add a task-specific solution function that returns a list of CartesianWaypoint objects from robolab_mp.types and call it from run_one_task.

The low-level follower is robolab_mp/planner.py. It sends absolute [x, y, z, qw, qx, qy, qz, gripper] actions through RoboLab's existing absolute differential IK controller. Targets are expressed in eef_frame; the planner converts orientation commands to the base_link frame expected by DroidIKActionCfg.