从人类行为中学习以实现人机协作运输中的主动辅助
Learning from Humans for Proactive Assistance in Human-Robot Collaborative Transport
- University of Michigan（密歇根大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
提出PROACT框架，通过预测人类协作行为并融入柔顺控制，在真实试验中显著降低交互功和完成时间，实现高效且响应的人机协作运输。
AI中文摘要：
我们聚焦于人机协作运输这一具有广泛相关性的挑战性任务，涵盖物流、制造和家庭场景，其中用户与机器人共同协作以搬运大型或重型物体。为了成为有效的合作伙伴，机器人应在保持对用户物理响应的同时，通过高效地协助物体搬运来减轻用户的负担。先前的工作通常分别处理这些能力，导致机器人可能高效移动物体但抵抗用户输入，或者适应用户但依赖持续引导。我们的关键洞察是，受障碍物约束的协作运输需要将人类协作行为的预测与柔顺机器人控制相结合。为此，我们提出了PROACT，一个用于人机协作运输的框架，通过人类协作行为的学习模型，将预期融入柔顺全身控制中。该框架基于大规模真实世界双人运输演示数据集进行训练，我们的Transformer架构将协作行为提炼为对未来物体运动的预测。在108次真实世界试验中，使用9自由度移动机械臂，相对于仅柔顺和MPC基线，PROACT将平均交互功分别降低了59.2%和20.4%，平均完成时间分别降低了12.9%和6.9%。我们实验的录像可在以下网址找到：此https URL。
英文摘要：
We focus on human-robot collaborative transport, a challenging task of broad relevance spanning logistics, manufacturing, and the home, in which a user and a robot work together to relocate a large or heavy object. To act as an effective partner, the robot should reduce the user's effort by contributing to efficient relocation of the object while remaining physically responsive to them. Prior work often addresses these capabilities separately, producing robots that may move the object efficiently but resist user input, or accommodate the user but depend on continuous guidance. Our key insight is that obstacle-constrained collaborative transport requires integrating predictions of human collaborative behavior with compliant robot control. To this end, we introduce PROACT, a framework for human-robot collaborative transport that incorporates anticipation into compliant whole-body control through a learned model of human collaborative behavior. Trained on a large-scale, real-world dataset of dyadic human transport demonstrations, our transformer architecture distills collaborative behavior into predictions of future object motion. Across 108 real-world trials with a 9-DoF mobile manipulator, PROACT reduces mean interaction work by 59.2\% and 20.4\%, and mean completion time by 12.9\% and 6.9\%, relative to compliance-only and MPC baselines, respectively. Footage from our experiments can be found at https://youtu.be/qAGvQfVPjbk.
