Update README.md

README.md

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----
-datasets:
-- IffYuan/Embodied-R1-Dataset
----
+---             
+  ---                                                                                                                                     
+  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
+
+  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
+
+  cd eval
+  python hf_inference_where2place.py
+  python hf_inference_vabench_point.py
+  ...
+
+  Related benchmarks:
+  - Embodied-R1-Dataset
+  - VABench-P
+  - VABench-V
+
+  ---
+  Training
+
+  Training scripts are available at:
+  https://github.com/pickxiguapi/Embodied-R1/tree/main/scripts
+
+  # 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
+
+  @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.
+
+  ---