Search is not available for this dataset
action listlengths 8 8 | observation.state listlengths 8 8 | observation.velocity listlengths 8 8 | episode_index int64 0 3.1k | frame_index int64 0 299 | index int64 0 429k | task_index int64 0 0 | timestamp float32 0 19.9 | next.done bool 2
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End of preview. Expand in Data Studio
This dataset was created using LeRobot.
Dataset Description
Banana-in-bowl manipulation rollouts collected in RoboLab (Isaac Lab) by a deterministic scripted oracle, at the hard initial-pose difficulty level.
How this data was made
Not a learned policy. A privileged-state waypoint-replay controller reads ground-truth object poses (no cameras) and replays an 8-waypoint grasp-and-place sequence, rate-limited to the same arm speed cap as our RFCL policies so trajectory length and smoothness are matched. The grasp recipe (a fixed end-effector orientation quaternion generalized over object yaw) comes from the CaP-X project; this dataset distills that recipe into a batch controller.
Difficulty level: hard
Difficulty is exactly two axes — object position half-range and yaw half-range:
| axis | value |
|---|---|
| position | +/-48 cm (1.00 x 48 cm) |
| yaw | +/-180 deg |
Levels come from the same definition across three tasks (position fraction 0.20 / 0.50 / 1.00 of a per-task feasible range; yaw 0 / 25 / 180 deg). Companion datasets: soft, medium, hard.
Contents
- 3102 episodes, 429493 frames, 15 fps.
- Two cameras at 270x480:
over_shoulder_left_camera,wrist_cam. - Constant-frame-rate normalized (required for torchcodec-based loaders) and shipped with q01/q99 statistics.
Action convention (read this before training)
actionis absolute joint targets, not deltas. Do not apply a delta transform on top.- 7 arm joints in droid frame (joint 7 carries a -pi/4 mount offset relative to the physical joint) + 1 gripper channel.
- Gripper is binary {0, 1}, not continuous.
- State vectors store the full physical joint vector (9 = 7 arm + 2 finger), unmodified.
Known biases and caveats
- Successes only. Episodes are kept only if the task succeeded under a K-hold settle criterion (success must hold for 15 consecutive steps). The oracle's success rate at this level was 0.580, so the realized initial-pose distribution is the solvable subset of the declared level, not the full declared range. This matters most at
hard. - Episodes are trimmed at first success, so lengths reflect time-to-solve. Median length grows with difficulty (soft 327k frames vs hard 429k frames for a similar episode count).
- The oracle is deterministic given an initial pose; diversity comes from the initial-pose distribution, not from policy stochasticity. Expect lower behavioral diversity than policy-generated rollouts.
- Depth and segmentation are not included.
Provenance
meta/provenance.json records the generating controller, that perception was privileged (ground-truth pose, no cameras), the grasp recipe reference, the arm speed cap, and the initial-pose setting.
License and attribution
Apache-2.0, inherited from RoboLab (Copyright NVIDIA CORPORATION & AFFILIATES, Apache License 2.0), the simulator and asset source used to generate these rollouts. Please attribute RoboLab / Isaac Lab when using this data.
Visualize
Open in the LeRobot dataset visualizer
- Homepage: [More Information Needed]
- Paper: [More Information Needed]
- License: apache-2.0
Dataset Structure
{
"codebase_version": "v3.0",
"robot_type": "unknown",
"total_episodes": 3102,
"total_frames": 429493,
"total_tasks": 1,
"chunks_size": 1000,
"data_files_size_in_mb": 100,
"video_files_size_in_mb": 200,
"fps": 15,
"splits": {
"train": "0:3102"
},
"data_path": "data/chunk-{chunk_index:03d}/file-{file_index:03d}.parquet",
"video_path": "videos/{video_key}/chunk-{chunk_index:03d}/file-{file_index:03d}.mp4",
"features": {
"action": {
"dtype": "float32",
"shape": [
8
],
"names": {
"motors": [
"j0",
"j1",
"j2",
"j3",
"j4",
"j5",
"j6",
"gripper"
]
},
"fps": 15
},
"observation.state": {
"dtype": "float32",
"shape": [
8
],
"names": {
"motors": [
"joint_0",
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"joint_2",
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}
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