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 5.94k | frame_index int64 0 287 | index int64 0 434k | task_index int64 0 0 | timestamp float32 0 19.1 | next.done bool 2
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End of preview. Expand in Data Studio
This dataset was created using LeRobot.
Dataset Description
RL rollouts from a RoboLab (Isaac Lab) BananaInBowl policy under the hard initial-pose
distribution, exported for pi0.5 delta-action SFT.
Provenance — read this before comparing against other runs in this line
The collecting policy is ipl-bana-hard-s0, an RFCL agent trained with a reverse→forward
transition threshold of solved_frac_threshold = 0.9 (forward phase begins once 45/50 demos
are solved in the reverse curriculum). Its final evaluation was success_end 0.98 with a mean
successful-episode length of 47.7 steps.
A separate lane (banana-hard-sf50) retrains this task from scratch with
solved_frac_threshold = 0.5. Datasets collected from that lane are not interchangeable with
this one: the policies differ, even though the task, initial-pose distribution and recording
conventions are identical. If you are assembling a controlled comparison, do not mix them.
The threshold affects only how the collecting policy was trained — it does not change anything about the recorded trajectories themselves.
Initial-pose distribution — hard
Object placements come from a pre-generated, physics-validated layout dataset
(DAVIAN-Robotics/robolab-init-positions) rather than being sampled at reset time, so no episode
starts with interpenetrating objects. hard means:
- position: 100% of the task's measured feasible range
- yaw: uniform over ±180° (full circle)
- containers whose name contains
binstay fixed; every other asset (includingbowl) is randomized together with the target
Stored yaw is absolute — it already includes each asset's scene-default orientation.
Recording conventions
| field | convention |
|---|---|
| arm action | absolute joint target in DROID frame (j7 offset by −π/4) |
| gripper action | binary {0, 1} (raw > 0 → close) |
| state | full joint vector (teleport-native) |
| success | K-hold settle, success_hold_steps = 15, episode truncated at success |
| cameras | over_shoulder_left_camera, wrist_cam |
| fps | 15 |
The binary gripper matters: an earlier round in this project recorded a continuous gripper and that overestimated downstream policy quality. Do not re-derive a continuous channel from it.
q01/q99 quantile statistics are present in meta/stats.json (required by pi0.5
quantile normalization) and were computed after aggregation, not per shard.
All videos are constant-frame-rate. Aggregation leaves PTS jitter that makes the torchcodec backend misread frames, so a CFR normalization pass was applied and verified (every video has a single PTS delta).
Contents
Collected by sampling from the policy distribution (temperature 1.0); a deterministic (argmax) counterpart is published separately. Only successful episodes are included — failures are discarded at collection time.
License and attribution
Fully synthetic, generated in simulation by this project; released under Apache-2.0. It contains no third-party recordings.
Size
- episodes: 5941
- frames: 433699
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": 5941,
"total_frames": 433699,
"total_tasks": 1,
"chunks_size": 1000,
"data_files_size_in_mb": 100,
"video_files_size_in_mb": 200,
"fps": 15,
"splits": {
"train": "0:5941"
},
"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": {
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}
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