Bimanual xArm Packing (Simulation)
Teleoperated demonstrations of a bimanual packing task in MuJoCo. Two xArm7 arms pack objects
(YCB and scanned household objects) into small, medium and large trays according to a goal. Each
episode is one subtask, such as "Pack the cracker box into the large tray.", and the task
field holds that instruction.
Layout
Each round folder is a complete LeRobot dataset (codebase version 2.1).
| Folder | Episodes | Frames |
|---|---|---|
baseline/round1 |
334 | 456,996 |
baseline/round2 |
336 | 291,368 |
baseline/round3 |
338 | 234,802 |
baseline/round4 |
339 | 329,912 |
beta-data/round1 |
384 | 430,226 |
beta-data/round3 |
522 | 399,971 |
beta-data/round4 |
540 | 428,902 |
baseline holds standard demonstrations. beta-data holds demonstrations collected while an
adversary perturbs the scene during the task, so episodes include recovery from disturbed
states. The beta-data rounds keep their original collection numbers.
Features
All rounds share one schema and are recorded at 60 Hz.
| Key | Shape | Description |
|---|---|---|
observation.state |
float32 (20,) | Per arm, left then right: end-effector position (x, y, z), rotation as the first two columns of the rotation matrix (r00, r10, r20, r01, r11, r21), gripper |
action |
float32 (20,) | Target pose in the same 20-d layout as the state |
observation.images.base |
video, 3x224x224 | Overhead camera |
observation.images.left_wrist |
video, 3x224x224 | Left wrist camera |
observation.images.right_wrist |
video, 3x224x224 | Right wrist camera |
task_index |
int64 | Index into meta/tasks.jsonl |
Videos are AV1-encoded MP4 files; decoding them needs an FFmpeg build with an AV1 decoder
(for example libdav1d). meta/high_level_prompts.json stores the goal-level prompt for each
episode.
Usage
Download one round and read its frames:
from huggingface_hub import snapshot_download
import pandas as pd
root = snapshot_download(
"kshitiz25/sim_packing_task",
repo_type="dataset",
allow_patterns="baseline/round1/*",
)
ds_root = f"{root}/baseline/round1"
frames = pd.read_parquet(f"{ds_root}/data/chunk-000/episode_000000.parquet")
Point a LeRobot release that reads codebase version 2.1 at ds_root to load a round as a
LeRobotDataset.
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