{"ID":2856152,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.11072","arxiv_id":"2510.11072","title":"PhysHSI: Towards a Real-World Generalizable and Natural Humanoid-Scene Interaction System","abstract":"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.","short_abstract":"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....","url_abs":"https://arxiv.org/abs/2510.11072","url_pdf":"https://arxiv.org/pdf/2510.11072v1","authors":"[\"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\"]","published":"2025-10-13T07:11:37Z","proceeding":"cs.RO","tasks":"[\"cs.RO\",\"cs.AI\",\"cs.LG\",\"eess.SY\"]","methods":"[]","has_code":false}