---
language:
- en
license: other
pipeline_tag: image-text-to-text
tags:
- robotics
- vision-language-model
- embodied-ai
- manipulation
- qwen2-vl
library_name: transformers
---

# Embodied-R1-3B-v1

**Embodied-R1: Reinforced Embodied Reasoning for General Robotic Manipulation (ICLR 2026)**

[[🌐 Project Website](https://embodied-r1.github.io)] [[📄 Paper](http://arxiv.org/abs/2508.13998)] [[🏆 ICLR2026 Version](https://openreview.net/forum?id=i5wlozMFsQ)] [[🎯 Dataset](https://huggingface.co/datasets/IffYuan/Embodied-R1-Dataset)] [[📦 Code](https://github.com/pickxiguapi/Embodied-R1)]

---

## Model Details

### Model Description

**Embodied-R1** is a 3B vision-language model (VLM) for general robotic manipulation.
It introduces a **Pointing** mechanism and uses **Reinforced Fine-tuning (RFT)** to bridge perception and action, with strong zero-shot generalization in embodied tasks.

![Embodied-R1 Framework](https://raw.githubusercontent.com/pickxiguapi/Embodied-R1/main/assets/r1_framework_readme.jpg)
*Figure: Embodied-R1 framework, performance overview, and zero-shot manipulation demos.*

### Model Sources

- **Repository:** https://github.com/pickxiguapi/Embodied-R1
- **Paper:** http://arxiv.org/abs/2508.13998
- **OpenReview:** https://openreview.net/forum?id=i5wlozMFsQ

### Updates

- **[2026-03]** VABench-P / VABench-V released:
  [VABench-P](https://huggingface.co/datasets/IffYuan/VABench-P), [VABench-V](https://huggingface.co/datasets/IffYuan/vabench-v)
- **[2026-03-03]** Embodied-R1 dataset released:
  https://huggingface.co/datasets/IffYuan/Embodied-R1-Dataset
- **[2026-01-27]** Accepted by ICLR 2026
- **[2025-08-22]** Embodied-R1-3B-v1 checkpoint released

---

## Intended Uses

### Direct Use

This model is intended for **research and benchmarking** in embodied reasoning and robotic manipulation tasks, including:
- Visual target grounding (VTG)
- Referring region grounding (RRG/REG-style tasks)
- Open-form grounding (OFG)

### Out-of-Scope Use

- Safety-critical real-world deployment without additional safeguards and validation
- Decision-making in high-risk domains
- Any use requiring guaranteed robustness under distribution shift

---

## How to Use

### Setup

```bash
git clone https://github.com/pickxiguapi/Embodied-R1.git
cd Embodied-R1

conda create -n embodied_r1 python=3.11 -y
conda activate embodied_r1

pip install transformers==4.51.3 accelerate
pip install qwen-vl-utils[decord]
```

### Inference

```bash
python inference_example.py
```

### Example Tasks

- VTG: *put the red block on top of the yellow block*
- RRG: *put pepper in pan*
- REG: *bring me the camel model*
- OFG: *loosening stuck bolts*

(Visualization examples are available in the project repo: `assets/`)

---

## Evaluation

```bash
cd eval
python hf_inference_where2place.py
python hf_inference_vabench_point.py
...
```

Related benchmarks:
- [Embodied-R1-Dataset](https://huggingface.co/datasets/IffYuan/Embodied-R1-Dataset)
- [VABench-P](https://huggingface.co/datasets/IffYuan/VABench-P)
- [VABench-V](https://huggingface.co/datasets/IffYuan/vabench-v)

---

## Training

Training scripts are available at:
https://github.com/pickxiguapi/Embodied-R1/tree/main/scripts

```bash
# Stage 1 training
bash scripts/stage_1_embodied_r1.sh

# Stage 2 training
bash scripts/stage_2_embodied_r1.sh
```

Key files:
- `scripts/config_stage1.yaml`
- `scripts/config_stage2.yaml`
- `scripts/stage_1_embodied_r1.sh`
- `scripts/stage_2_embodied_r1.sh`
- `scripts/model_merger.py` (checkpoint merging + HF export)

---

## Limitations

- Performance may vary across environments, camera viewpoints, and unseen object domains.
- Outputs are generated from visual-language reasoning and may include localization/action errors.
- Additional system-level constraints (calibration, motion planning, safety checks) are required for real robot deployment.

---

## Citation

```bibtex
@article{yuan2026embodied,
  title={Embodied-r1: Reinforced embodied reasoning for general robotic manipulation},
  author={Yuan, Yifu and Cui, Haiqin and Huang, Yaoting and Chen, Yibin and Ni, Fei and Dong, Zibin and Li, Pengyi and Zheng, Yan and Tang, Hongyao and Hao, Jianye},
  journal={The Fourteenth International Conference on Learning Representations},
  year={2026}
}

@article{yuan2026seeing,
  title={From seeing to doing: Bridging reasoning and decision for robotic manipulation},
  author={Yuan, Yifu and Cui, Haiqin and Chen, Yibin and Dong, Zibin and Ni, Fei and Kou, Longxin and Liu, Jinyi and Li, Pengyi and Zheng, Yan and Hao, Jianye},
  journal={The Fourteenth International Conference on Learning Representations},
  year={2026}
}
```

---

## Acknowledgements

If this model or resources are useful for your research, please consider citing our work and starring the repository.