外观
论文arXiv 2610.1989614 天前
通过人机交接主动感知上肢功能性活动能力
原文链接：Active Perception of Upper-Limb Functional Mobility through Human-Robot Handovers（arXiv 2610.19896 · 发布日期：2026-10-14）
Active Perception of Upper-Limb Functional Mobility through Human-Robot Handovers
针对协作机器人适应上肢损伤者能力的问题，提出基于重复交接请求的在线主动感知，并在仿真和物理平台验证。
核心要点
- 共享工作空间中的协作机器人需根据人类伙伴的上肢能力调整行为。
- 将上肢功能活动范围表示为削减健康关节限位的损伤系数。
- 机器人通过重复交接请求主动探测，并在线更新损伤系数的贝叶斯信念。
- 仿真与物理概念验证表明，流程可在真实交接中准确恢复损伤系数。
验证范围与局限：验证包括仿真研究和一个机器人平台上采用人为扮演损伤的概念验证物理实验，任务为人机交接。
英文摘要 (Abstract)
The reintegration of people in the workplace after an upper-limb injury is often a long process. Collaborative robots and humanoids operating in shared workspaces must adapt their behavior to the physical capabilities of their human partners. Achieving this requires robots to autonomously estimate task-relevant human mobility during interaction, without relying on external clinical assessments. In this work, we formulate the estimation of upper-limb functional mobility as an active perception problem. We model task-available upper-limb functional ranges of motion through impairment coefficients that parameterize reductions of healthy joint limits, and integrate this representation into a human model used by the robot during interaction. Based on this formulation, we propose an online active perception pipeline in which the robot actively probes the user through repeated handover requests while progressively updating a Bayesian belief over the impairment coefficients. This enables the robot to continuously refine its internal model of the user's joint-level capabilities through interaction rather than passive observation. The proposed framework was evaluated through simulation studies and a proof-of-concept physical validation with a robotic platform and enacted human impairments, demonstrating that the pipeline can accurately recover the impairment coefficients during real human-robot handovers.
作者：Francesco Semeraro, Marta Lagomarsino, Alberto Ranavolo, Arash Ajoudani
来源
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