README.md

---
pretty_name: RoboValue Training and Small-Scale Evaluation Data
language:
- en
- zh
tags:
- robotics
- lerobot
- robovalue
- value-model-benchmark
- training-data
- evaluation-data
- diagnostic-test-set
- simulation
- real-world
- full-shot
- one-shot
configs:
- config_name: real-world-test-30
  default: true
  data_files:
  - split: test
    path: evaluation/real-world/test-30/episode_catalog.parquet
- config_name: simulation-test-30
  data_files:
  - split: test
    path: evaluation/simulation/test-30/episode_catalog.parquet
---

<div align="center">
  <img src="https://huggingface.co/datasets/wzlllong/value-model-benchmark-training-data/resolve/main/assets/robovalue-logo.png" alt="RoboValue" width="420" />
  <h1>RoboValue Training and Small-Scale Evaluation Data</h1>
  <p><strong>Training Demonstrations · Small-Scale Real-World &amp; Simulation Public Test Sets</strong></p>
  <p>
    <a href="https://arxiv.org/abs/2610.26663"><img src="https://img.shields.io/badge/arXiv-Paper-red?logo=arxiv" alt="RoboValue paper on arXiv" /></a>
    <a href="https://github.com/RoboValue-Benchmark/RoboValue"><img src="https://img.shields.io/badge/GitHub-RoboValue-24292F?logo=github" alt="RoboValue GitHub" /></a>
    <a href="https://robovalue-benchmark.github.io/doc/"><img src="https://img.shields.io/badge/Documentation-8A2BE2?logo=readthedocs&amp;logoColor=white" alt="Documentation" /></a>
    <a href="https://robovalue-benchmark.github.io/"><img src="https://img.shields.io/badge/Website-527BC3?logo=googlechrome&amp;logoColor=white" alt="Website" /></a>
  </p>
  <p><strong>English</strong> · <a href="https://huggingface.co/datasets/wzlllong/value-model-benchmark-training-data/blob/main/README_zh.md">简体中文</a></p>
</div>

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. Two **small-scale public diagnostic test sets** are organized separately under `evaluation/`, with **30 real-world trajectories and 30 simulation trajectories**. Each domain has four tasks and 90 synchronized camera videos. Together the test subsets contain **60 trajectories, 44,647 time steps, and 180 videos**. [The paper](https://arxiv.org/abs/2610.26663) and current leaderboard report **Zero-Shot and One-Shot evaluations**.

<p align="center">
  <a href="#overview">Overview</a> · <a href="#simulation">Simulation</a> · <a href="#real-world">Real-World Training</a> · <a href="#public-evaluation">Public Test Sets</a> · <a href="#metric-summary">Metrics</a> · <a href="#repository-layout">Repository</a> · <a href="#download-and-reading">Download &amp; Read</a>
</p>

## 📊 1. Dataset Overview <a name="overview"></a>

| Domain | Platform | <div align="right">Tasks</div> | <div align="right">Full-Shot (episodes)</div> | <div align="right">One-Shot (episodes)</div> |
| --- | --- | ---: | ---: | ---: |
| 🟦 [**Simulation**](#simulation) | ARX | <div align="right"><strong>15</strong></div> | <div align="right"><strong>1,500</strong></div> | <div align="right"><strong>15</strong></div> |
| 🟧 [**Real-World**](#real-world) | AgiBot Genie02 | <div align="right"><strong>20</strong></div> | <div align="right"><strong>2,000</strong></div> | <div align="right"><strong>20</strong></div> |

| 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 training subsets, the release contains **3,535 episodes, 2,348,375 synchronized time steps, and 10,605 camera videos**. The two One-Shot subsets account for **35 episodes, 24,442 time steps, and 105 camera videos**.


> **HF preview:** The explicit `real-world-test-30/test` and `simulation-test-30/test` configurations each contain **one row per test trajectory (30 rows)**. Select a configuration to browse its task, trajectory type, applicable metrics, and three camera video paths. Real-world is the default. These catalogs are browsing indices; native per-frame model input is read with LeRobot. Auxiliary JSON reports and navigation documents are excluded from the preview.

### Public Test Sets <a name="public-evaluation"></a>

Training demonstrations and public test trajectories have separate directories and purposes:

| Collection | Location | Scale | Purpose |
| --- | --- | --- | --- |
| Simulation / real-world training | `training-data/<domain>/{full-shot,one-shot}` | Four training subsets listed above | Expert demonstrations for training or reference conditioning |
| [Real-world public test set](evaluation/real-world/test-30/README.md) | `evaluation/real-world/test-30` | **4 tasks / 30 trajectories / 27,498 time steps / 90 videos** | Small-scale In-Domain diagnostic evaluation |
| [Simulation public test set](evaluation/simulation/test-30/README.md) | `evaluation/simulation/test-30` | **4 tasks / 30 trajectories / 17,149 time steps / 90 videos** | Small-scale In-Domain diagnostic evaluation |

The real-world subset contains **12 desk**, **12 flower-and-diffuser**, **3 drum**, and **3 button** trajectories. The simulation subset contains **12 pen-holder**, **12 mug-hanging**, **3 press-by-number**, and **3 block-swapping** trajectories. Both supply images and English instructions, with evaluation annotations stored separately. Each subset has its own local task and episode indices, independent of training and of the other test subset. They provide partial diagnostic coverage rather than a complete leaderboard test release.

| Coverage | Real-world | Simulation |
| --- | --- | --- |
| TGA-CT, TGA-CF, VOC, Cycle-VOC, VS | Each uses 4 ED episodes | Each uses 4 ED episodes |
| SA / SIA | 24 / 12 episodes | 24 / 12 episodes |
| TRR | 12 episodes / 4 three-role groups | 12 episodes / 4 three-role groups |
| CSVC | 12 episodes / 10 solution groups | 12 episodes / 6 order groups |
| FPL | 8 ER/EC episodes | Original FPL fields retained; no formal FPL cohort |
| VOC-MEM | 6 complete counting histories | 6 complete memory histories; no annotated counting-subtask intervals |

See the [real-world metric coverage and trajectory index](evaluation/real-world/test-30/README.md#metric-coverage) and [simulation metric coverage and trajectory index](evaluation/simulation/test-30/README.md#metric-coverage). Simulation ED/MS terminal labels and semantic alignment are user-attested; TRR/MEM terminal-label evidence retains the source status of pending independent verification. The simulation README documents these annotation evidence states.

**Subtask annotations:** All six training/test roots provide `annotations/subtasks.json` with S-numbered definitions, source references, and original label IDs where needed. Simulation One-Shot also provides 58 segments and 8,080 frame labels; both test roots provide all existing subtask segments. Other training roots provide reference definitions only. See [Subtask reading](SUBTASKS.md) for file formats, temporal-label availability, and loading examples.

<a id="metric-summary"></a>

### Metric summary

These one-sentence definitions follow the paper’s §4.3 (pp. 9–12). **↑ Higher is better; FPL ↓ lower is better.**

| Metric | Brief definition |
| --- | --- |
| **SA · Success Accuracy ↑** | Measures the fraction of same-task successful/unsuccessful episode pairs in which the successful episode has the higher average terminal score. |
| **TGA-CT · Task Grounding Accuracy - Cross-Task ↑** | Measures how often the correct instruction gives a larger start-to-end value gain than every cross-task negative instruction for the same successful episode. |
| **TGA-CF · Task Grounding Accuracy - Counterfactual ↑** | Measures how often the correct instruction gives a larger start-to-end value gain than every counterfactual instruction changing the object, action, placement, or constraint. |
| **SIA · Subtask Identification Accuracy ↑** | Scores subtask recognition as the geometric mean of the independent judge’s probabilities for the correct subtask, based on the model’s free-form descriptions. |
| **VOC · Value-Order Correlation ↑** | Measures the Spearman rank correlation between predicted values and normalized time along successful trajectories. |
| **Cycle-VOC ↑** | Averages Spearman correlations with forward and reversed progress order when a successful trajectory is replayed forward then backward as one continuous sequence. |
| **Memory-VOC (VOC-MEM) ↑** | Applies VOC’s Spearman score to successful long trajectories with recurring visual states, using the history available to each model interface. |
| **FPL · Failure-Point Localization ↓** | Measures the absolute time error between the annotated error onset and the onset inferred from the largest detected value decline, normalized by trajectory duration. |
| **TRR · Trajectory Recovery Reasoning ↑** | Checks matched trajectories for the expected stage trends: failure down, continued error non-increasing, recovery attempt up, and successful/failed recovery outcome up/down. |
| **VS · Value Stability ↑** | Combines trend efficiency with non-flat time coverage to reward stable value updates while penalizing reversals and prolonged flat intervals. |
| **CSVC · Cross-Solution Value Consistency ↑** | Measures the consistency of local value gains for semantically matched subtasks across valid execution solutions. |

The paper’s **Memory-VOC** is labeled **VOC-MEM** in this dataset, with membership key `voc_mem`. Stored labels and keys retain these names. VOC and VOC-MEM annotations are sampling anchors, not calibrated per-frame value scores.

## 🟦 2. Simulation Training <a name="simulation"></a>

### 2.1 Overview

Simulation demonstrations use the **ARX** robot platform. They are based on the open-source [**RoboDojo**](https://huggingface.co/datasets/RoboDojo-Benchmark/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** |

<details>
<summary><strong>Task List · 15 tasks</strong></summary>

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 |

[Full-Shot task metadata](https://huggingface.co/datasets/wzlllong/value-model-benchmark-training-data/blob/main/training-data/simulation/full-shot/meta/tasks.parquet) · [One-Shot task metadata](https://huggingface.co/datasets/wzlllong/value-model-benchmark-training-data/blob/main/training-data/simulation/one-shot/meta/tasks.parquet)

</details>

<details>
<summary><strong>State Reference · 14 dimensions</strong></summary>

**`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` |

[Full-Shot metadata](https://huggingface.co/datasets/wzlllong/value-model-benchmark-training-data/blob/main/training-data/simulation/full-shot/meta/info.json) · [One-Shot metadata](https://huggingface.co/datasets/wzlllong/value-model-benchmark-training-data/blob/main/training-data/simulation/one-shot/meta/info.json)

</details>

<details>
<summary><strong>Action Reference · 14 dimensions</strong></summary>

**`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` |

[Full-Shot metadata](https://huggingface.co/datasets/wzlllong/value-model-benchmark-training-data/blob/main/training-data/simulation/full-shot/meta/info.json) · [One-Shot metadata](https://huggingface.co/datasets/wzlllong/value-model-benchmark-training-data/blob/main/training-data/simulation/one-shot/meta/info.json)

</details>

<details>
<summary><strong>Camera Reference · 3 RGB streams</strong></summary>

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]` |

[Full-Shot metadata](https://huggingface.co/datasets/wzlllong/value-model-benchmark-training-data/blob/main/training-data/simulation/full-shot/meta/info.json) · [One-Shot metadata](https://huggingface.co/datasets/wzlllong/value-model-benchmark-training-data/blob/main/training-data/simulation/one-shot/meta/info.json)

</details>

### ▦ 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 →**](https://huggingface.co/datasets/wzlllong/value-model-benchmark-training-data/tree/main/training-data/simulation/full-shot) · [Download and read](#download-and-reading)

### ◉ 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 →**](https://huggingface.co/datasets/wzlllong/value-model-benchmark-training-data/tree/main/training-data/simulation/one-shot) · [Download and read](#download-and-reading)

**Subtask annotations:** The separate adaptation supplement retains **58 subtask segments and 8,080 frame labels** in `annotations/subtasks.json`, `annotations/subtask_segments.parquet`, and `annotations/frame_labels.parquet`. These files are separate from training model observations and test-set labels; reading instructions are linked above.

## 🟧 3. Real-World Training <a name="real-world"></a>

### 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** |

<details>
<summary><strong>Task List · 20 tasks</strong></summary>

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 |

[Full-Shot task metadata](https://huggingface.co/datasets/wzlllong/value-model-benchmark-training-data/blob/main/training-data/real-world/full-shot/meta/tasks.parquet) · [One-Shot task metadata](https://huggingface.co/datasets/wzlllong/value-model-benchmark-training-data/blob/main/training-data/real-world/one-shot/meta/tasks.parquet)

</details>

<details>
<summary><strong>State Reference · 290 dimensions</strong></summary>

**`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.

[Full-Shot metadata](https://huggingface.co/datasets/wzlllong/value-model-benchmark-training-data/blob/main/training-data/real-world/full-shot/meta/info.json) · [One-Shot metadata](https://huggingface.co/datasets/wzlllong/value-model-benchmark-training-data/blob/main/training-data/real-world/one-shot/meta/info.json)

</details>

<details>
<summary><strong>Action Reference · 52 dimensions</strong></summary>

**`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.

[Full-Shot metadata](https://huggingface.co/datasets/wzlllong/value-model-benchmark-training-data/blob/main/training-data/real-world/full-shot/meta/info.json) · [One-Shot metadata](https://huggingface.co/datasets/wzlllong/value-model-benchmark-training-data/blob/main/training-data/real-world/one-shot/meta/info.json)

</details>

<details>
<summary><strong>Camera Reference · 3 RGB streams</strong></summary>

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]` |

[Full-Shot metadata](https://huggingface.co/datasets/wzlllong/value-model-benchmark-training-data/blob/main/training-data/real-world/full-shot/meta/info.json) · [One-Shot metadata](https://huggingface.co/datasets/wzlllong/value-model-benchmark-training-data/blob/main/training-data/real-world/one-shot/meta/info.json)

</details>

### ▦ 3.2 Full-Shot

**20 tasks × 100 demonstrations = 2,000 episodes.**

| ▦ Time steps | ▶ Videos | ▣ Size | ◷ Total duration | ⑂ Training split |
| ---: | --- | ---: | ---: | --- |
| **1,534,321** | **6,000 · HEVC** | **≈23.13 GB** | ≈14.21 hours | `train: 0:2000` |

[**Browse files →**](https://huggingface.co/datasets/wzlllong/value-model-benchmark-training-data/tree/main/training-data/real-world/full-shot) · [Download and read](#download-and-reading)

### ◉ 3.3 One-Shot

**20 tasks × 1 demonstration = 20 episodes.** Uses the same task definitions as Full-Shot.

| ▦ Time steps | ▶ Videos | ▣ Size | ◷ Total duration | ⑂ Training split |
| ---: | --- | ---: | ---: | --- |
| **16,362** | **60 · HEVC** | **≈0.22 GB** | ≈9.09 minutes | `train: 0:20` |

[**Browse files →**](https://huggingface.co/datasets/wzlllong/value-model-benchmark-training-data/tree/main/training-data/real-world/one-shot) · [Download and read](#download-and-reading)

## 🗂️ 4. Repository Layout <a name="repository-layout"></a>

```text
README.md                         English dataset card
README_zh.md                      Chinese dataset card
subset_catalog.json               Subset index
training-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
evaluation/
├── simulation/test-30/           30 trajectories · 4 tasks
│   └── meta/, data/, videos/, annotations/, README files and catalog
└── real-world/test-30/            30 trajectories · 4 tasks
    ├── meta/                     Native LeRobot v3 metadata
    ├── data/                     Frame references
    ├── videos/                   Three synchronized camera streams
    ├── annotations/              Separate evaluation labels
    ├── episode_catalog.parquet   One browsing row per trajectory
    ├── README.md / README_zh.md   Task and metric documentation
    └── checksums.sha256           File integrity manifest
```

Each released training or public test subset directory is an independent **LeRobot** dataset root (`root`). Both test subsets have frame references, RGB videos, and English instructions, with evaluation labels alongside them; they have no robot state/action vectors. The primary view is the head camera for real-world and the front camera for simulation.

The repository-level training directory is `training-data/`. Each LeRobot root retains its native `data/` frame-table directory; for example, Simulation One-Shot frame tables are stored under `training-data/simulation/one-shot/data/`.

<details>
<summary><strong>Training subset files and metadata</strong></summary>

| 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 |
| `meta/conversion.json` | Data conversion, video handling, and statistics notes |
| `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](https://huggingface.co/docs/lerobot/lerobot-dataset-v3) · [Machine-readable subset catalog](https://huggingface.co/datasets/wzlllong/value-model-benchmark-training-data/blob/main/subset_catalog.json)

</details>

## ↓ 5. Download and Reading <a name="download-and-reading"></a>

### 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** | `training-data/simulation/full-shot` | 1,500 / 789,612 | ≈45.47 GB |
| 🟦 **Simulation One-Shot** | `training-data/simulation/one-shot` | 15 / 8,080 | ≈0.50 GB |
| 🟧 **Real-World Full-Shot** | `training-data/real-world/full-shot` | 2,000 / 1,534,321 | ≈23.13 GB |
| 🟧 **Real-World One-Shot** | `training-data/real-world/one-shot` | 20 / 16,362 | ≈0.22 GB |
| **Real-World Public Test** | `evaluation/real-world/test-30` | 30 / 27,498 | ≈0.38 GB |
| **Simulation Public Test** | `evaluation/simulation/test-30` | 30 / 17,149 | ≈0.595 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 training subsets, change `prefix` to the corresponding training path in the table. For the public test set, use the separate image-and-instruction example below. 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**:

```bash
python -m pip install "lerobot==0.4.2" "av==15.1.0"
```

```python
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 = "training-data/simulation/one-shot"
revision = "main"  # Pin the current commit hash for reproducible runs.
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:

```text
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 `revision`; pin the current commit hash for reproducible runs.


### 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`:

```python
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:

```bash
shasum -a 256 -c checksums.sha256
```

### 5.5 Download and read a public test subset

Choose `evaluation/real-world/test-30` or `evaluation/simulation/test-30` to download one public test subset. The example is pinned to `578593ef28ee7a2d861ebf38162c928291b0d4a9`, which contains both test domains; downloading and loading use the same revision. Training examples use the current `main` revision; pin its commit hash for reproducible runs.

```python
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 = "evaluation/real-world/test-30"
revision = "578593ef28ee7a2d861ebf38162c928291b0d4a9"
snapshot = snapshot_download(
    repo_id=repo_id, repo_type="dataset", revision=revision,
    allow_patterns=[f"{prefix}/**"], local_dir="./robovalue-data",
)
root = Path(snapshot) / prefix
dataset = LeRobotDataset(
    repo_id=repo_id, root=root, revision=revision,
    video_backend="pyav", tolerance_s=1e-4,
)
sample = dataset[0]
value_input = {
    "images": {camera: sample[camera] for camera in dataset.meta.camera_keys},
    "instruction": sample["task"],
}
print(dataset.num_episodes, dataset.num_frames)  # real-world: 30 27498; simulation: 30 17149
```

The native metadata retains `train: 0:30` as its LeRobot container split. Both public test subsets are intended for **testing**, and the HF browsing index uses the semantic `test` split. Load evaluation annotations explicitly from `root / "annotations"`; do not pass labels, success flags, trajectory types, metric memberships, or frame references to the model. History-based models can use `delta_timestamps` within each episode. See the [real-world README](evaluation/real-world/test-30/README.md) or [simulation README](evaluation/simulation/test-30/README.md) for annotation joins, metric cohorts, and integrity checks. The catalogs share 12 columns; `video_primary` refers to head/front respectively, and the native camera keys preserve those view meanings.

**Publication authorization:** All data in this dataset were authorized before public release.