README.md

---
# 可选：数据集分类（非必填，但建议加）
task_categories:
  - robotics  # 示例：机器人学，可替换为自己的分类（如nlp、cv等）

# 可选：数据集语言（非必填）
language:
  - en  # 示例：英语，可替换为zh、fr等

# 必选：gated access 核心配置 - 用户需同意的条款
extra_gated_prompt: "You agree not to use this dataset for commercial purposes, but only for academic research."

# 必选：自定义收集的问卷字段（核心）
extra_gated_fields:
  # 自定义字段1：公司/机构（示例）
  Company/Organization:
    type: text  # 字段类型：text（文本框）、country（国家选择器）、select（下拉框）等
    description: "填写示例：ETH Zurich、波士顿动力、独立研究者"  # 字段说明，引导用户填写

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  # 自定义字段3：使用目的（示例）
  Intended use:
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    description: "填写示例：模仿学习、策略泛化、双手操作研究"

  # 可追加更多自定义字段，比如：
  # Research Team:
  #   type: text
  #   description: "Your research team name (e.g., Robotics Lab at XXX University)"
  
license: mit
datasets:
- your-huggingface-org/MOVE
---

<!-- ---
license: mit
datasets:
- your-huggingface-org/MOVE
--- -->

# Dataset Card for MOVE Real-World Manipulation Dataset

## MOVE: Motion-Based Variability Enhancement for Spatial Generalization in Robotic Manipulation

Jointly Released by:

> ### 🎓 清华大学 (Tsinghua University)
> ### 🤖 智源人工智能研究院 (Beijing Academy of Artificial Intelligence - BAAI)

This Hugging Face Dataset Card describes the **Real-World Robotic Manipulation Dataset** collected using the **MOVE (Motion-Based Variability Enhancement)** paradigm, as presented in the paper "MOVE: A Simple Motion-Based Data Collection Paradigm for Spatial Generalization in Robotic Manipulation."

The core value of the MOVE paradigm is the injection of **dynamic feature augmentation** into the environment (objects and camera) during expert demonstrations. This process captures a richer variety of spatial configurations within a single trajectory, significantly improving policy performance, especially in terms of **spatial generalization** to unseen locations and **data efficiency**.

## 💾 Dataset Structure

This dataset focuses on the **Real-World Pick-and-Place** task. All data was collected using the **Piper robotic arm** teleoperated via a **Pika device** under dynamic environment configurations.

### Data Fields

Each trajectory contains a sequence of timesteps, recording the essential observations and state information required for robotic policy learning.

| Field Key | Data Type | Description |
| :--- | :--- | :--- |
| `timestep_id` | `int` | Sequential timestep index within the trajectory. |
| **`camera/color/Camera`** | `PIL.Image` / `ndarray` | **RGB Image Observation**. Due to the MOVE paradigm, the image captures dynamically changing object, target, and camera viewpoints. |
| **`arm/jointStatePosition/joint_single`** | `array[float]` | **Robot Joint State/Action**. The joint positions of the Piper robotic arm, representing the robot's state or executed action at this timestep. |
| **`/arm/jointStatePosition/master`** | `array[float]` | **Master Device State**. The joint or position states of the Pika teleoperation master device, recording the human operator's intent. |

> **Note:** This dataset strictly includes only the three key data streams listed above. It does not include explicit 3D coordinates, world-frame camera poses, or other calculated metadata.

### Data Splits

The real-world dataset is split based on the total number of environment interaction steps (timesteps), allowing for efficiency evaluation:

| Split Name | Task | Total Timesteps | Description |
| :--- | :--- | :--- | :--- |
| `real_world_35k` | Real-World Pick-and-Place (e.g., Orange to Tray) | **35,000** | A challenging, low-data scenario for testing spatial generalization capability. |
| `real_world_75k` | Real-World Pick-and-Place | **75,000** | Used for performance scaling and efficiency comparison against static baselines. |

## 🚀 Key Advantages

* **High Spatial Generalization:** Policies trained on this dynamically augmented data demonstrate superior success rates when tested on spatially randomized, unseen configurations.
* **Superior Data Efficiency:** MOVE datasets enable policies to achieve competitive performance with a significantly lower total number of timesteps compared to datasets collected using the traditional static approach.

## 🎯 Usage

This dataset is an ideal resource for training robust real-world robotic manipulation policies, particularly those that rely on high-generalization visual-motor data.

### Loading the Data

```python
from datasets import load_dataset

# Load the 75k timestep real-world subset
dataset = load_dataset("your-huggingface-org/MOVE", "real_world_75k")

# Access image and state data
first_example = dataset["train"][0]
image = first_example["camera/color/Camera"]
robot_state = first_example["arm/jointStatePosition/joint_single"]
```

## Citation

if you find this work helpful, please consider citing our paper:

```
@misc{wang2025movesimplemotionbaseddata,
      title={MOVE: A Simple Motion-Based Data Collection Paradigm for Spatial Generalization in Robotic Manipulation}, 
      author={Huanqian Wang and Chi Bene Chen and Yang Yue and Danhua Tao and Tong Guo and Shaoxuan Xie and Denghang Huang and Shiji Song and Guocai Yao and Gao Huang},
      year={2025},
      eprint={2512.04813},
      archivePrefix={arXiv},
      primaryClass={cs.RO},
      url={https://arxiv.org/abs/2512.04813}, 
}
```