Openpi Comet: Competition Solution For 2025 BEHAVIOR Challenge
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 π_{0.5}, we focus on systematically building our solution by studying the effects of training techniques and data. Through careful ablations, we show the scaling power in pre-training and post-training phases for competitive 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.
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Junjie Bai
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Yu-Wei Chao
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Qizhi Chen
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2025-12-31T20:58:08.066Z
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Qizhi Chen
Tavish9
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Tavish9
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Jinwei Gu
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Moo Jin Kim
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Zhaoshuo Li
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Xuan Li
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Tsung-Yi Lin
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Ming-Yu Liu
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Nic Ma
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Kaichun Mo
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Delin Qu
claimed_verified
2025-12-16T09:48:41.492Z
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Delin Qu
delinqu
user
delinqu
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Shangkun Sun
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Hongchi Xia
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Fangyin Wei
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Xiaohui Zeng
Openpi Comet: Competition Solution For 2025 BEHAVIOR Challenge
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 π_{0.5}, we focus on systematically building our solution by studying the effects of training techniques and data. Through careful ablations, we show the scaling power in pre-training and post-training phases for competitive 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.
The 2025 BEHAVIOR Challenge addresses long-horizon tasks for physical agents in simulated environments, with a focus on household tasks and mobile manipulation, achieving strong performance through systematic training technique analysis and foundation model adaptation.
long-horizon tasks physical agents simulated environments household tasks mobile manipulation embodied AI foundation models pre-training post-training training techniques data