Add robotics metadata and link to paper/code

README.md

@@ -1,10 +1,52 @@
 ---
 datasets:
 - behavior-1k/2025-challenge-demos
+pipeline_tag: robotics
+license: apache-2.0
+tags:
+- embodied-ai
+- vla
+- behavior-1k
 ---
 
-This is the model weights of Team Comet for the [2025 BEHAVIOR Challenge](https://behavior.stanford.edu/index.html). Our [submission](https://behavior.stanford.edu/challenge/leaderboard.html#privileged-information-track) achieved a Q-score of 0.2514, securing 2nd place overall and finishing just behind the winning team by a narrow margin—highlighting both the strong competitiveness of our approach and the effectiveness of our end-to-end VLA training strategy.
+# Openpi Comet: Competition Solution for 2025 BEHAVIOR Challenge
 
-Please refer to [this repository](https://github.com/mli0603/openpi-comet) for detailed usage. It provides a unified framework for pre-training, post-training, data generation and evaluation of π0.5 (Pi05) models on BEHAVIOR-1K.
+This repository contains the model weights of Team Comet for the [2025 BEHAVIOR Challenge](https://behavior.stanford.edu/index.html). Our [submission](https://behavior.stanford.edu/challenge/leaderboard.html#privileged-information-track) achieved a Q-score of 0.2514, securing 2nd place overall and finishing just behind the winning team by a narrow margin—highlighting both the strong competitiveness of our approach and the effectiveness of our end-to-end Vision-Language-Action (VLA) training strategy.
 
-The [tech report](https://arxiv.org/abs/2512.10071) is available now. 
+- **Paper:** [Openpi Comet: Competition Solution For 2025 BEHAVIOR Challenge](https://arxiv.org/abs/2512.10071)
+- **Repository:** [https://github.com/mli0603/openpi-comet](https://github.com/mli0603/openpi-comet)
+- **Project Page:** [BEHAVIOR Challenge](https://behavior.stanford.edu/index.html)
+
+## Model Description
+
+OpenPi Comet is a foundation model designed for long-horizon mobile manipulation tasks in realistic household settings, specifically developed for the BEHAVIOR-1K dataset. Building on $\pi_{0.5}$ (Pi05), the model incorporates hierarchical instructions (global, subtask, skill) and multimodal observations (RGB, depth, point cloud, etc.). Through systematic training techniques and data scaling, including Rejection Sampling Fine-Tuning (RFT), the team achieved a validation Q-score of 0.345.
+
+## Usage
+
+Please refer to [the official GitHub repository](https://github.com/mli0603/openpi-comet) for detailed installation and usage instructions. The codebase provides a unified framework for:
+- Distributed pre-training and fine-tuning of OpenPi models.
+- Data generation (teleoperation and simulation rollouts).
+- Post-training via Rejection Sampling Fine-Tuning (RFT).
+- Evaluation within the BEHAVIOR-1K simulator.
+
+### Quick Setup
+```bash
+git clone https://github.com/mli0603/openpi-comet.git
+cd openpi-comet
+uv sync
+uv pip install -e .
+```
+
+## Citation
+
+If you find this work useful, please consider citing:
+
+```bibtex
+@article{bai2025openpicometcompetitionsolution,
+  title={Openpi Comet: Competition Solution For 2025 BEHAVIOR Challenge}, 
+  author={Junjie Bai and Yu-Wei Chao and Qizhi Chen and Jinwei Gu and Moo Jin Kim and Zhaoshuo Li and Xuan Li and Tsung-Yi Lin and Ming-Yu Liu and Nic Ma and Kaichun Mo and Delin Qu and Shangkun Sun and Hongchi Xia and Fangyin Wei and Xiaohui Zeng},
+  journal={arXiv preprint arXiv:2512.10071},
+  year={2025},
+  url={https://arxiv.org/abs/2512.10071}, 
+}
+```