Pro-HOI：感知引导的人形-物体交互
Pro-HOI: Perceptive Root-guided Humanoid-Object Interaction
AI总结：
Pro-HOI通过感知引导框架实现人形机器人稳健的移动- manipulation 交互，利用根轨迹作为通用接口，提升复杂环境下的任务执行能力。
摘要：
执行可靠的仿人机器人-物体交互（HOI）任务受到缺乏通用控制接口和稳健闭环感知机制的阻碍。在本文中，我们引入了感知引导的人形-物体交互（Pro-HOI），一种适用于稳健人形移动- manipulation 的通用框架。首先，我们收集了适用于现实世界部署的箱子搬运动作，并通过有符号距离场损失优化穿透伪影。其次，我们提出了一种新的训练框架，该框架将策略条件于期望的根轨迹，同时仅利用参考运动作为奖励。这种设计不仅消除了对复杂奖励调优的需求，还使根轨迹成为高层次规划器的通用接口，实现了同时导航和移动- manipulation。此外，为了确保操作可靠性，我们整合了一个持久物体估计模块。通过融合实时检测与数字孪生，该模块使机器人能够自主检测滑动并触发再抓取动作。在Unitree G1机器人上的实证验证表明，Pro-HOI在泛化性和鲁棒性方面显著优于基线，实现了在复杂现实场景中的可靠长时程执行。
英文摘要：
Executing reliable Humanoid-Object Interaction (HOI) tasks for humanoid robots is hindered by the lack of generalized control interfaces and robust closed-loop perception mechanisms. In this work, we introduce Perceptive Root-guided Humanoid-Object Interaction, Pro-HOI, a generalizable framework for robust humanoid loco-manipulation. First, we collect box-carrying motions that are suitable for real-world deployment and optimize penetration artifacts through a Signed Distance Field loss. Second, we propose a novel training framework that conditions the policy on a desired root-trajectory while utilizing reference motion exclusively as a reward. This design not only eliminates the need for intricate reward tuning but also establishes root trajectory as a universal interface for high-level planners, enabling simultaneous navigation and loco-manipulation. Furthermore, to ensure operational reliability, we incorporate a persistent object estimation module. By fusing real-time detection with Digital Twin, this module allows the robot to autonomously detect slippage and trigger re-grasping maneuvers. Empirical validation on a Unitree G1 robot demonstrates that Pro-HOI significantly outperforms baselines in generalization and robustness, achieving reliable long-horizon execution in complex real-world scenarios.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
