Datasets:
pretty_name: Value Model Benchmark Training Data
language:
- en
- zh
tags:
- robotics
- lerobot
- robovalue
- value-model-benchmark
- training-data
- simulation
- real-world
- full-shot
- one-shot
Training Data for Robotic Value Models
Simulation & Real-World · Full-Shot & One-Shot
English · 简体中文
RoboValue provides expert demonstrations for robotic value models, covering 15 simulation tasks and 20 real-world tasks. Both domains offer Full-Shot and One-Shot subsets in the shared LeRobot Dataset v3.0 format.
All four training subsets are publicly available. The paper and current leaderboard report Zero-Shot and One-Shot evaluations.
Overview · Simulation · Real-World · Repository · Download & Read
📊 1. Dataset Overview
| Domain | Platform | Tasks |
Full-Shot (episodes) |
One-Shot (episodes) |
|---|---|---|---|---|
| 🟦 Simulation | ARX | 15 |
1,500 |
15 |
| 🟧 Real-World | AgiBot Genie02 | 20 |
2,000 |
20 |
| Setting | Demonstrations | Use |
|---|---|---|
| ▦ Full-Shot | 100 per task | Training or fine-tuning on the complete expert collection |
| ◉ One-Shot | 1 per task | Reference conditioning or task-specific adaptation |
Across all four subsets, the release contains 3,535 episodes, 2,345,122 synchronized time steps, and 10,605 camera videos. The two One-Shot subsets account for 35 episodes, 24,523 time steps, and 105 camera videos.
HF preview: The automatic preview counts video files as rows, including one showcase clip. Use the episode and time-step totals here and the LeRobot loader for training.
🟦 2. Simulation
2.1 Overview
Simulation demonstrations use the ARX robot platform. They are based on the open-source RoboDojo dataset. Our team reviewed the data and recorded selected demonstrations again. They cover 15 manipulation tasks at 25 FPS.
Trajectory Contents · Shared by Full-Shot and One-Shot
In both Full-Shot and One-Shot, an episode is a complete task demonstration containing the task instruction, per-step states and actions, identifiers and timestamps, and three RGB camera streams. Episode metadata records its length, data-row range, and video segments for each camera.
| Content | Representation |
|---|---|
| ∿ Robot state | observation.state: float32[14] |
| ➜ Robot action | action: float32[14] |
| ▶ RGB observations | High, left-wrist, and right-wrist views, each 640 × 480 |
| ☰ Instruction and identity | Task instruction, task_index, episode_index, frame_index |
| ◷ Time | timestamp in seconds; 25 FPS |
Task List · 15 tasks
The simulation dataset covers the following 15 tasks.
| Index | Task |
|---|---|
| 0 | Arrange Largest Number |
| 1 | Fill Pen Holder |
| 2 | Fold Clothes |
| 3 | Hang Mugs |
| 4 | Insert Tubes |
| 5 | Make Toast |
| 6 | Organize Table |
| 7 | Pack Objects Into Box |
| 8 | Play Stacking Toy |
| 9 | Press By Number |
| 10 | Put Bottles Into Dustbin |
| 11 | Stack Blocks |
| 12 | Store Laptop And Headphones |
| 13 | Swap Blocks |
| 14 | Sweep Blocks |
State Reference · 14 dimensions
observation.state · float32[14]
The first seven entries are left-side fields; the last seven are right-side fields.
| Dimension indices (0-based) | Field names |
|---|---|
| 0–6 | left_joint_0 … left_joint_6 |
| 7–13 | right_joint_0 … right_joint_6 |
Action Reference · 14 dimensions
action · float32[14]
The first seven entries are left-side fields; the last seven are right-side fields.
| Dimension indices (0-based) | Field names |
|---|---|
| 0–6 | left_joint_0 … left_joint_6 |
| 7–13 | right_joint_0 … right_joint_6 |
Camera Reference · 3 RGB streams
Loaded images use CHW (channels, height, width) order.
| Camera key | Loaded shape |
|---|---|
observation.images.cam_high |
[3, 480, 640] |
observation.images.cam_left_wrist |
[3, 480, 640] |
observation.images.cam_right_wrist |
[3, 480, 640] |
▦ 2.2 Full-Shot
15 tasks × 100 demonstrations = 1,500 episodes.
| ▦ Time steps | ▶ Videos | ▣ Size | ◷ Total duration | ⑂ Training split |
|---|---|---|---|---|
| 789,612 | 4,500 · AV1 / H.264 | ≈45.47 GB | ≈8.77 hours | train: 0:1500 |
Browse files → · Download and read
◉ 2.3 One-Shot
15 tasks × 1 demonstration = 15 episodes. Uses the same task definitions as Full-Shot.
| ▦ Time steps | ▶ Videos | ▣ Size | ◷ Total duration | ⑂ Training split |
|---|---|---|---|---|
| 8,080 | 45 · AV1 | ≈0.50 GB | ≈5.39 minutes | train: 0:15 |
Browse files → · Task–episode–row mapping · Download and read
🟧 3. Real-World
3.1 Overview
Real-world demonstrations are collected with the AgiBot Genie02 robot. They cover 20 manipulation tasks, including object placement and sorting, household organization, drawers, repeated presses, kitchen activities, and packaging, at 30 FPS.
Trajectory Contents · Shared by Full-Shot and One-Shot
In both Full-Shot and One-Shot, an episode is a complete task demonstration containing the task instruction, per-step robot observations and actions, identifiers and timestamps, and RGB videos from the head, left wrist, and right wrist. Episode metadata records its length, data-row range, and video segments for each camera.
| Content | Representation |
|---|---|
| ∿ Robot state | observation.state: float32[290], including joint/end-effector state and auxiliary fields |
| ➜ Robot action | action: float32[52], including joint targets, end-effector target poses, and other control fields |
| ▶ RGB observations | Head 640 × 400; each wrist 640 × 528 |
| ☰ Instruction and identity | Task instruction, task_index, episode_index, frame_index |
| ◷ Time | timestamp in seconds; 30 FPS |
Task List · 20 tasks
The real-world dataset covers the following 20 tasks.
| Index | Task |
|---|---|
| 0 | Transfer Blocks Using Plate |
| 1 | Enter Keypad Number Sequence |
| 2 | Set the Table |
| 3 | Arrange Seasoning Jars |
| 4 | Load Plastic Plates Into Rack |
| 5 | Press Button Five Times |
| 6 | Place Pot On Induction Cooker |
| 7 | Organize Desktop Workstation |
| 8 | Organize Books And Pen |
| 9 | Arrange Family Tea Set |
| 10 | Operate Drawers In Sequence |
| 11 | Sort Blocks in Order |
| 12 | Fill And Place Pen Holder |
| 13 | Organize Cosmetics Storage Box |
| 14 | Strike Drum Five Times |
| 15 | Apply Dish Soap Repeatedly |
| 16 | Season Meat Skewers Repeatedly |
| 17 | Pack Items For Shipping |
| 18 | Arrange Flowers and Reed Diffuser |
| 19 | Prepare Breakfast |
State Reference · 290 dimensions
observation.state · float32[290]
| Dimension indices (0-based) | Meaning |
|---|---|
| 0–1 | Left/right effector state |
| 2–25 | End-effector force/torque fields |
| 26–31 | Arm end-effector XYZ positions, m, base_link |
| 32–55 | End-effector linear/angular velocity fields |
| 56–63 | Arm end-effector XYZW orientations, base_link |
| 108–121 | Arm joint positions, rad |
| 122–135 | Arm joint torques, Nm |
| 136–149 | Arm joint velocities, rad/s |
| 164–175 | Head joint positions, velocities, efforts, and modes |
| 176–195 | Waist joint positions, velocities, efforts, and modes |
| 196–202 | Robot position and orientation in odom |
The table lists key fields; the full 290-dimensional definition is in the metadata below.
Action Reference · 52 dimensions
action · float32[52]
End-effector action targets are computed by forward kinematics from joint actions. Positions use meters in base_link, and orientations use XYZW quaternions. Grippers use their original opening or stroke values. The 52-dimensional action vector includes end-effector poses, joint targets, and other control fields; dimension indices are listed below.
| Dimension indices (0-based) | Meaning |
|---|---|
| 0–1 | Left/right effector target opening or stroke |
| 2–7 | Arm end-effector target XYZ positions, m, base_link |
| 8–15 | Arm end-effector target XYZW orientations, base_link |
| 16–29 | Arm joint target positions, rad |
| 30–32 | Head joint target positions, rad |
| 33–37 | Waist joint target positions, rad |
| 38–43 | Base target XYZ linear and XYZ angular velocity |
| 44–47 | Left reference-pose XYZW quaternion |
| 48–51 | Right reference-pose XYZW quaternion |
The last two reference orientations are distinct from the end-effector target orientations. Full field definitions are in the metadata below.
Camera Reference · 3 RGB streams
Loaded images use CHW (channels, height, width) order.
| Camera key | Loaded shape |
|---|---|
observation.images.cam_high |
[3, 400, 640] |
observation.images.cam_left_wrist |
[3, 528, 640] |
observation.images.cam_right_wrist |
[3, 528, 640] |
▦ 3.2 Full-Shot
20 tasks × 100 demonstrations = 2,000 episodes.
| ▦ Time steps | ▶ Videos | ▣ Size | ◷ Total duration | ⑂ Training split |
|---|---|---|---|---|
| 1,530,987 | 6,000 · HEVC | ≈23.23 GB | ≈14.18 hours | train: 0:2000 |
Browse files → · Download and read
◉ 3.3 One-Shot
20 tasks × 1 demonstration = 20 episodes. Uses the same task definitions as Full-Shot.
Each task selects the trajectory with the smallest original episode_id among its 100 Full-Shot source trajectories. Original IDs and source conversion indices are recorded separately in the mapping below.
| ▦ Time steps | ▶ Videos | ▣ Size | ◷ Total duration | ⑂ Training split |
|---|---|---|---|---|
| 16,443 | 60 · HEVC | ≈0.22 GB | ≈9.14 minutes | train: 0:20 |
Browse files → · Task–episode–row mapping · Download and read
🗂️ 4. Repository Layout
README.md English dataset card
README_zh.md Chinese dataset card
subset_catalog.json Subset index
data/
├── simulation/
│ ├── full-shot/ 1,500 episodes · 15 tasks
│ └── one-shot/ 15 episodes · 1 per task
└── real-world/
├── full-shot/ 2,000 episodes · 20 tasks
└── one-shot/ 20 episodes · 1 per task
Each subset directory is an independent LeRobot dataset root (root), containing per-step Parquet data, three original camera streams, and metadata.
Subset files and metadata
| File or directory | Purpose |
|---|---|
data/chunk-*/file-*.parquet |
Per-step states, actions, identifiers, and timestamps |
videos/<camera>/chunk-*/file-*.mp4 |
RGB camera video streams |
meta/info.json |
Format version, features, dimensions, FPS, and path templates |
meta/tasks.parquet |
Task instructions and indices |
meta/episodes/chunk-*/file-*.parquet |
Episode length, task, data-row boundaries, video offsets, and episode statistics |
meta/stats.json |
Dataset-level feature statistics |
provenance/ |
Episode sources and task correspondence |
checksums.sha256 |
File integrity manifest |
Data rows span [dataset_from_index, dataset_to_index); video segments are recorded separately for each camera. Time steps and duration are counted once per episode.
Official LeRobot v3.0 format guide · Machine-readable subset catalog
↓ 5. Download and Reading
5.1 Choose a subset
Select one prefix to download only that subset. Sizes use decimal GB.
| 🗂️ Subset | ↓ prefix |
▦ Episodes / time steps | ▣ Size |
|---|---|---|---|
| 🟦 Simulation Full-Shot | data/simulation/full-shot |
1,500 / 789,612 | ≈45.47 GB |
| 🟦 Simulation One-Shot | data/simulation/one-shot |
15 / 8,080 | ≈0.50 GB |
| 🟧 Real-World Full-Shot | data/real-world/full-shot |
2,000 / 1,530,987 | ≈23.23 GB |
| 🟧 Real-World One-Shot | data/real-world/one-shot |
20 / 16,443 | ≈0.22 GB |
5.2 Download and read
This example loads the smaller Simulation One-Shot subset and returns states, actions, and all three camera frames. To switch subsets, change prefix to the corresponding path in the table. Public data can be downloaded anonymously, and this example automatically reuses an existing local Hugging Face login. If anonymous downloads are rate-limited, run hf auth login and retry.
The examples were tested with Python 3.12, LeRobot 0.4.2, and PyAV 15.1.0:
python -m pip install "lerobot==0.4.2" "av==15.1.0"
import os
from pathlib import Path
from huggingface_hub import snapshot_download
from lerobot.datasets.lerobot_dataset import LeRobotDataset
repo_id = "wzlllong/value-model-benchmark-training-data"
prefix = "data/simulation/one-shot"
revision = "fe1248b75878fc31451b50b1676a53abe1e7297d"
local_dir = Path(os.environ.get("ROBOVALUE_LOCAL_DIR", "./robovalue-data"))
snapshot = snapshot_download(
repo_id=repo_id,
repo_type="dataset",
revision=revision,
allow_patterns=[f"{prefix}/**"],
local_dir=local_dir,
)
dataset = LeRobotDataset(
repo_id=repo_id,
root=Path(snapshot) / prefix,
revision=revision,
video_backend="pyav",
)
sample = dataset[0]
print("episodes / frames:", dataset.num_episodes, dataset.num_frames)
print("action / state:", tuple(sample["action"].shape),
tuple(sample["observation.state"].shape))
for camera in dataset.meta.camera_keys:
print(camera, tuple(sample[camera].shape))
Output:
episodes / frames: 15 8080
action / state: (14,) (14,)
observation.images.cam_high (3, 480, 640)
observation.images.cam_left_wrist (3, 480, 640)
observation.images.cam_right_wrist (3, 480, 640)
Files are saved under ./robovalue-data. Set the ROBOVALUE_LOCAL_DIR environment variable to an existing download directory to reuse local files. Download and reading use the same fixed data revision.
5.3 Use a training batch
All Full-Shot tasks have exactly 100 episodes in the train split: 15 simulation tasks (1,500 episodes) and 20 real-world tasks (2,000 episodes). Each One-Shot subset has exactly one episode per task.
After the example above, use the LeRobot dataset directly with a PyTorch DataLoader:
from torch.utils.data import DataLoader
loader = DataLoader(dataset, batch_size=2, shuffle=True, num_workers=0)
batch = next(iter(loader))
states = batch["observation.state"]
actions = batch["action"]
images = {camera: batch[camera] for camera in dataset.meta.camera_keys}
instructions = batch["task"]
print("batch action / state:", tuple(actions.shape), tuple(states.shape))
Simulation One-Shot prints batch action / state: (2, 14) (2, 14). For real-world data, the corresponding shapes are (2, 52) and (2, 290).
batch["task"] retains the source task text from meta/tasks.parquet; task_index matches the task lists above.
Each batch contains states, actions, task instructions, and three camera views. Configure the model's input and output dimensions for the selected domain. The training method constructs value or progress targets from these expert demonstrations.
Each time step is one training item. For temporal windows, configure LeRobot delta_timestamps.
5.4 Integrity checks
Each subset contains checksums.sha256. Run this command from the subset directory:
shasum -a 256 -c checksums.sha256
Publication authorization: All data in this dataset were authorized before public release.