root string | codebase_version string | total_episodes_info int64 | total_frames_info int64 | episode_rows int64 | episode_length_sum int64 | dataset_len int64 | data_files int64 | video_files int64 | unique_video_files_by_key dict | episodes_per_data_file_max int64 | episodes_per_video_file_max_by_key dict | video_path_template string | data_path_template string | samples list |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
/home/zrgong/projects/RMBench-Plus-Plus/generated_data_lerobot_v3_put_back_block_relocate_v1/put_back_block_relocate | v3.0 | 100 | 47,155 | 100 | 47,155 | 47,155 | 100 | 400 | {
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"observation.images.left_camera": 100,
"observation.images.right_camera": 100,
"observation.images.front_camera": 100
} | 1 | {
"observation.images.head_camera": 1,
"observation.images.left_camera": 1,
"observation.images.right_camera": 1,
"observation.images.front_camera": 1
} | videos/{video_key}/chunk-{chunk_index:03d}/file-{file_index:03d}.mp4 | data/chunk-{chunk_index:03d}/file-{file_index:03d}.parquet | [
{
"index": 0,
"episode_index": 0,
"frame_index": 0,
"observation_state_shape": [
14
],
"action_shape": [
14
],
"image_shapes": {
"observation.images.head_camera": [
3,
240,
320
],
"observation.images.left_camera": [
3,
... |
RMBench++ Put Back Block Relocate v1
This pilot dataset contains 100 official-RT scripted-expert demonstrations for
the frozen put_back_block_relocate training split.
The policy must pick up the block, move it to the center, press a button with the left hand, and remember the brief post-button feedback. If one mat lights up, the block must be placed on that newly indicated mat. If no mat lights up, the block must be returned to the originally indicated mat.
Canonical Task Prompt
Pick up the block, move it to the center, press the button, watch for a brief mat signal, and put the block back on the remembered target mat.
Frozen Coverage And Contents
- 100 training episodes using the frozen v1 training manifest.
- Four initial mats are balanced: 25 episodes each.
- Five relocate outcomes are balanced: left, right, front, back, and no-light occur 20 times each.
- The effective target mat is balanced: 25 episodes each.
- One Parquet file per episode and one H.264 MP4 per episode per camera.
- Native RMBench 14D
observation.stateand 14Daction. - Policy RGB views: head, left wrist, and right wrist. The front camera is included only for QA and visualization.
- Per-frame episode seed, canonical prompt provenance, JSON-encoded
rmbench_plusaudit metadata, and memory-keyframe sidecars undermeta/.
The embedded meta/rmbench_plus_manifest.json contains the frozen train and
eval seed specifications. This repository publishes training demonstrations
only; eval expert trajectories are not included.
Memory Keyframes
meta/memory_keyframes.json uses schema rmbench_memory_keyframes_v1. Each
episode contains task/keyframe labels for discriminator-style FULL-frame
retention training:
initial_beliefcenter_placebutton_press_downmemory_update_targetormemory_confirm_no_update- terminal
final_place
The companion meta/memory_keyframe_audit.json records role counts and protocol
version.
Loading
from lerobot.datasets.lerobot_dataset import LeRobotDataset
dataset = LeRobotDataset(
repo_id="zrgong/rmbench-plus-plus-put-back-block-relocate",
video_backend="pyav",
)
Validation And Provenance
Before upload, the production gate required 100/100 training scripted-expert success, 100/100 private eval scripted-expert preflight success, exact manifest metadata for every episode, native 14D reads, one data shard per episode, four video streams per episode, and a complete LeRobot v3 validation summary.
The exact RMBench++ Git revision used for production is stored in
meta/rmbench_plus_source_revision.txt.
These demonstrations are training and scripted-expert evidence. They are not closed-loop policy evaluation results, and this v1 pilot is not a general-memory leaderboard.
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