Openpi Comet: Competition Solution For 2025 BEHAVIOR Challenge
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Abstract (arXiv)
The 2025 BEHAVIOR Challenge is designed to rigorously track progress toward solving long-horizon tasks by physical agents in simulated environments. BEHAVIOR-1K focuses on everyday household tasks that people most want robots to assist with and these tasks introduce long-horizon mobile manipulation challenges in realistic settings, bridging the gap between current research and real-world, human-centric applications. This report presents our solution to the 2025 BEHAVIOR Challenge in a very close 2nd place and substantially outperforms the rest of the submissions. Building on π0.5, we focus on systematically building our solution by studying the effects of training techniques and data. Through careful ablation studies, we reveal the scaling benefits in both the pre-training and post-training phases, leading to a validation Q-score of 0.345, significantly surpassing previous state-of-the-art performance. We summarize our practical lessons and design recommendations that we hope will provide actionable insights for the broader embodied AI community when adapting powerful foundation models to complex embodied scenarios. Project page: https://github.com/mli0603/openpi-comet
Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). First posted on arXiv 10 Dec 2025.
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Citing papers per year: 2026: 8.
8 papers.
- 2026G0.5: One Autoregressive Stream for Robot Reasoning and Actioncited by 17
- 2026AutoDrive-P3: Unified Chain of Perception-Prediction-Planning Thought via Reinforcement Fine-Tuningcited by 13
- 2026How VLAs (Really) Work In Open-World Environmentscited by 3
- 2026EmbodiedSWE: Coding Agents for Long Horizon Dexterous Roboticscited by 2
- 2026Make Your VLA More Robust Without More Data By Interleaving Motion Planningcited by 1
- 2026FurnitureVLA: Learning Long-Horizon Bimanual Furniture Assembly with Vision-Language-Action Modelcited by 1
- 2026Behavior-Skill: A Fine-Grained Benchmark for Evaluating Vision-Language-Action Policies in Long-Horizon Taskscited by 0
- 2026Sensing Which Modality Matters: Evidence-Gated Regularization for Robust VLA Policiescited by 0
- 2026EmbodiedSWE: Coding Agents for Long Horizon Dexterous Robotics23 Sep 2026
- 2026Sensing Which Modality Matters: Evidence-Gated Regularization for Robust VLA Policies2 Sep 2026
- 2026Behavior-Skill: A Fine-Grained Benchmark for Evaluating Vision-Language-Action Policies in Long-Horizon Tasks31 Aug 2026
- 2026G0.5: One Autoregressive Stream for Robot Reasoning and Action12 Aug 2026
- 2026FurnitureVLA: Learning Long-Horizon Bimanual Furniture Assembly with Vision-Language-Action Model1 Jul 2026
- 2026Make Your VLA More Robust Without More Data By Interleaving Motion Planning31 May 2026
- 2026How VLAs (Really) Work In Open-World Environments23 Apr 2026
- 2026AutoDrive-P3: Unified Chain of Perception-Prediction-Planning Thought via Reinforcement Fine-Tuning30 Mar 2026