B1k_Recovery

Failure-recovery and demonstration data for BEHAVIOR-1K, in LeRobot v3.0 format, organised one subfolder per task.

B1k_Recovery/
└── task5/
    └── Demo+Recovery+Success/    setting_mousetraps — human demos + nav + place + success
        ├── data/    5,795 Parquet shards (chunk-000 … chunk-005)
        ├── videos/  17,385 MP4 files (3 camera keys × 6 chunks)
        └── meta/    info.json, tasks.parquet, episodes/, conversion_manifest.jsonl

Further tasks will be added as sibling folders (task10/, …). Each subfolder is a self-contained LeRobot v3 dataset: point your loader at the innermost dataset folder (e.g. task5/Demo+Recovery+Success), not at the repository root.

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task5 — setting_mousetraps

Training-ready mixed dataset combining official human demonstrations with generated navigation, placement/recovery, and successful-rollout episodes.

Composition

Source family	Episodes	Frames	Hours @ 30 fps
Official 2026 human demonstrations	200	2,039,222	18.882
Generated navigation	1,170	562,590	5.209
Generated success demonstrations	37	327,684	3.034
Generated placement / recovery	4,388	607,744	5.627
Total	5,795	3,537,240	32.752

Roughly 58% of frames are official human demonstration; the remaining 42% is generated navigation, placement/recovery, and success data.

Episode lengths are strongly bimodal: 5,102 of 5,795 episodes are under 500 frames (short recovery clips, ~25% of all frames), while 237 episodes exceed 3,000 frames (long demonstrations, ~67% of all frames). Sampling is per-frame, so a uniformly shuffled batch reflects the frame split, not the episode split.

• LeRobot codebase format: v3.0
• Robot: R1Pro
• Task: setting_mousetraps, stored as local task_index=0
• Observation state: float32[61]
• Action: float32[23]
• Frequency: 30 Hz
• RGB: three 224×224 H.264 (yuv420p) streams
• Size on disk: 7.3 GB across 23,192 files
• Frame-sample count after action-horizon truncation (H=30): 3,363,390

Observation state (61-D, BEHAVIOR 2026 layout)

Slice	Meaning
0:3	base-frame observed base_qvel = [vx, vy, wz]
3:10, 10:17	left arm qpos, qvel
17:20, 20:24	left end-effector position, quaternion
24:26, 26:28	left gripper qpos, qvel
28:35, 35:42	right arm qpos, qvel
42:45, 45:49	right end-effector position, quaternion
49:51, 51:53	right gripper qpos, qvel
53:57, 57:61	trunk qpos, qvel

These are observed simulator states in physical velocity units, not normalized actions.

For generated sources retaining the legacy 256-D proprioception, the builder extracts the documented blocks and rotates the holonomic virtual-joint velocity at legacy indices 253:256 into the robot/base frame using the virtual-joint yaw at index 246:

vx_base =  cos(yaw) * vx_world + sin(yaw) * vy_world
vy_base = -sin(yaw) * vx_world + cos(yaw) * vy_world
wz_base =  wz_world

Official 61-D human states are preserved directly.

Action (23-D R1Pro VLA action)

base[0:3], trunk[3:7], left_arm[7:14], left_gripper[14],
right_arm[15:22], right_gripper[22]

Cameras

• observation.rgb.zed_link_camera_0 (head)
• observation.rgb.left_realsense_link_camera_0
• observation.rgb.right_realsense_link_camera_0

Generated sources contain no depth or robot-to-camera pose, so those fields are omitted rather than fabricated.

Transition fields

Every row carries next.reward, next.terminated, next.truncated, plus the standard LeRobot v3 timestamp, frame, episode, global, and task indices.

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Usage notes (read before training)

1. Remap task_index — this is the most common way to get this wrong. meta/tasks.parquet maps setting_mousetraps → 0, a dataset-local index. The BEHAVIOR-1K global index for this activity is 5. A pipeline that feeds the stored task_index straight to the model will train it as task 0, and — because stage count is keyed off the task id — will use 5 stages instead of 12, while evaluation conditions on task 5. Remap via the activity name (tasks.parquet ↔ task_data.json), not the stored integer.

2. base_qvel is base-frame. Do not re-rotate it. Verified empirically on this dataset: std = (0.0694, 0.0292, 0.0762), std_x/std_y = 2.37, and |vx| > |vy| in 77.7% of frames. A world-frame dataset would show a ratio near 1.0 and no forward bias. If your evaluator has a frame switch (e.g. B1K_BASE_QVEL_FRAME), leave it unset/base; forcing world feeds a rotated signal to a base-frame-trained policy.

3. Use qvel-corrected normalization statistics. Norm stats whose state[0:3] std is ~10× smaller than their own action[0:3] std were computed on a corrupted qvel channel. Achieved and commanded base velocity must have comparable magnitude; on this data the correct scale is std ≈ (0.109, 0.061, 0.133).

4. Known bias: the base channel has a strong stand-still prior. 81.6% of frames have |state_vx| < 0.005, and of those only ~2.0% command |action_vx| > 0.05. corr(state[0:3], action[0:3]) = 0.88 / 0.79 / 0.68. A policy can score low base-velocity loss by echoing the current velocity rather than learning when to start moving, which at evaluation can present as a robot that turns in place but does not drive. Worth measuring open-loop before concluding a checkpoint is healthy. The generated recovery episodes reduce this leakage — the human-only subset is markedly worse (corr ≈ 0.997, and only 0.05% of stationary frames command motion).

Validation

Validated before publication:

• all 5,795 Parquets and all 3,537,240 state/action rows scanned;
• every state 61-D, every action 23-D, all values finite;
• episode/frame/global indices, timestamps, task mapping, and metadata totals agree;
• all 17,385 videos decoded and checked against episode frame counts;
• exact source-to-output checks passed for human, navigation, success, and placement episodes;
• samples from every source family loaded through LeRobot 0.5.2 with all three RGB tensors.

The conversion manifest is at task5/Demo+Recovery+Success/meta/conversion_manifest.jsonl. The converter is omnigibson/learning/embodiedClaw_recovery/scripts/export/build_lerobot_2026.py in the associated BEHAVIOR-1K challenge codebase.

Models trained on this data

Checkpoints fine-tuned from BEHAVIOR-1K checkpoint_1 on task5 are published at fastwalker1118/b1kcheckpoints (task5ckptmerged15000 … 39999). Each bundles its own assets/ norm stats, so evaluation picks up matching normalization automatically. 30,000 steps at batch 128 is ≈1.14 epochs over this dataset.

Compatibility warning

This is the 2026 format. Do not train a legacy 2025 pipeline expecting a 256-D state, world/canonical-frame base qvel, or observation.images.rgb.* camera keys on this data without an explicit format adapter.