Search is not available for this dataset
observation.state list | action list | timestamp float32 0 6.95 | frame_index int64 0 139 | episode_index int64 0 819 | index int64 0 105k | task_index int64 0 0 |
|---|---|---|---|---|---|---|
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End of preview. Expand in Data Studio
grasp03-sim-real-v6
LeRobot dataset for top-down mug grasping with an xArm7 + WujiHand robot. Combines sim trajectories (Genesis simulator, raytraced) and real-world teleoperation demos. Sim trajectories were re-rendered with a new pipeline that matches the smoothing applied during real-world replay, so sim and real share similar signal characteristics.
- Total: 820 episodes
- Task:
Grasp mug from top and lift - FPS: 20
- State / action: 27-dim each (7 arm + 20 hand DOFs, hand in DFS order)
- Cameras:
observation.images.third_person,observation.images.wrist(640×480)
Episode layout
| Index range | Count | Source | Notes |
|---|---|---|---|
| 0..781 | 782 | sim (kinematic replay, v6 pipeline) | 394 xy ±5 cm + 388 xy ±10 cm |
| 782..783 | 2 | real (OLD), stride=2 | ~109 frames each |
| 784..791 | 8 | real, /workspace/rank2_4/ |
no stride, ~132 frames each |
| 792..819 | 28 | real, grasp_data_real/run_20260523_* |
no stride, ~130 frames each |
v6 sim pipeline (kinematic replay)
For every sim episode, the source arm_reach_sim_log.npz is preprocessed
before kinematic teleport + render:
- Stationary-frame filter (
max_keep=5): drops redundant frames wheremax|arm_t - arm_{t-1}| ≤ 1e-3andmax|hand_t - hand_{t-1}| ≤ 5e-3, keeping at most 5 consecutive stationary frames per hold run. - Centered moving-average smoothing (
window=7, passes=2, preserve_ends) on:robot_dofs[:, :7](arm state)robot_dofs[:, 7:](hand state)ctrl_target[:, :7](arm command, used asaction)ctrl_target[:, 7:](hand command)obj_pose[:, :3](object position)obj_pose[:, 3:7](object quaternion; renormalized after smoothing)
- Kinematic teleport: at each kept frame, set robot DOFs to smoothed
robot_dofs, place object at smoothedobj_pose, then render both cameras.
Hand DOFs are converted from sim's BFS layout to the dataset's DFS layout via
PERM_BFS_TO_DFS = [5*j + i for i in range(5) for j in range(4)].
This pipeline parallels the centered moving-average smoothing applied by
replay_xarm_wuji_openloop.py (--arm-joint-smooth-window 7 --passes 2) when
the same sim trajectories were physically replayed on the real robot to record
the 28 real demos.
Scene randomization
For each scene group (object + pedestal_h + xy + euler), three mutually distinct random plastic colors are sampled (pairwise RGB L2 ≥ 0.4) for:
- the mug (object)
- the pedestal (box under the mug)
- the xArm7+WujiHand robot
Sim background: gray dome. Pedestal: 12×12 cm box, height varies in [0.13, 0.17] m.
Camera serials (real)
- Third-person:
213522251462 - Wrist:
128422272701
Object
Mug: 94134b02-c73e-4f2c-ad1d-a00a78160d98.glb (scale varies per episode).
License
Apache 2.0
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