PhysHSI：実世界で汎化可能な自然なヒューマノイド・シーンインタラクションシステム
PhysHSI: Towards a Real-World Generalizable and Natural Humanoid-Scene Interaction System
シミュレーションでの敵対的動作事前分布に基づく方策学習と、LiDARとカメラを組み合わせた粗から細への物体位置推定により、ヒューマノイドが実世界で物体運搬や着座などの多様なインタラクションを自然に自律実行できるシステムを構築した。
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著者: Huayi Wang, Wentao Zhang, Runyi Yu, Tao Huang, Junli Ren, Feiyu Jia, Zirui Wang, Xiaojie Niu, Xiao Chen, Jiahe Chen, Qifeng Chen, Jingbo Wang, Jiangmiao Pang
分類: cs.RO, cs.AI, cs.LG, cs.SY, eess.SY
原文アブストラクト
Deploying humanoid robots to interact with real-world environments--such as carrying objects or sitting on chairs--requires generalizable, lifelike motions and robust scene perception. Although prior approaches have advanced each capability individually, combining them in a unified system is still an ongoing challenge. In this work, we present a physical-world humanoid-scene interaction system, PhysHSI, that enables humanoids to autonomously perform diverse interaction tasks while maintaining natural and lifelike behaviors. PhysHSI comprises a simulation training pipeline and a real-world deployment system. In simulation, we adopt adversarial motion prior-based policy learning to imitate natural humanoid-scene interaction data across diverse scenarios, achieving both generalization and lifelike behaviors. For real-world deployment, we introduce a coarse-to-fine object localization module that combines LiDAR and camera inputs to provide continuous and robust scene perception. We validate PhysHSI on four representative interactive tasks--box carrying, sitting, lying, and standing up--in both simulation and real-world settings, demonstrating consistently high success rates, strong generalization across diverse task goals, and natural motion patterns.
