通过基于观测器的主动辅助手部引导实现可扩展的运动觉示教

中文摘要 AI 辅助

通过机器人手部引导进行的运动觉示教为模仿学习和示教编程提供了一种自然的演示收集接口。然而，长时间的示教会话会导致操作者疲劳，降低演示质量并限制其可扩展性。当前工业手部引导方法通常不提供主动辅助，而替代方案要么需要昂贵的腕部安装力-力矩传感器，要么依赖于无法用于新任务的学习运动先验。我们提出RHOAS，这是一种无需额外硬件、基于模型力估计主动支持操作者预期运动的手部引导方案。我们将机器人手部引导视为操作者主动控制的交互，而非与被动环境的交互。标准手部引导方法通常依赖于基于被动性的柔顺控制架构，这会不必要地增加操作者的体力消耗，限制可演示运动的范围，且在主动交互中无法提供预期的稳定性保证。相反，我们的设计利用基于模型的外力矩估计、内部关节力矩传感以及冗余机器人运动学，在人类交互频率带宽内主动支持人类的物理输入。我们解决了基于观测器的力估计所带来的实际挑战，包括抑制未建模的关节弹性动态效应和反馈路径中的测量噪声、在运动学奇异点附近降低的估计精度，以及静态重力补偿误差。在针对16名参与者使用KUKA LWR iiwa开展的用户研究中，我们证明了体力消耗的统计显著降低、精确和敏捷任务的可操作性提升，以及明确的用户偏好。

英文摘要

Kinesthetic teaching through robot hand-guiding provides a natural interface for collecting demonstrations in imitation learning and programming-by-demonstration. However, extended sessions cause operator fatigue, reducing demonstration quality and limiting scalability. Current industrial hand-guiding approaches typically provide no active assistance, and alternatives require costly wrist-mounted force-torque sensors or rely on learned motion priors unavailable for new tasks. We propose RHOAS, a hand-guiding scheme that actively supports operator-intended motions using model-based force estimation without additional hardware. Our approach considers robot hand-guiding as an actively controlled interaction by the human operator, rather than an interaction with a passive environment. Standard methods used for hand-guiding typically rely on general passivity-based compliant control architectures that unnecessarily increase operator effort and limit the range of demonstrable motions without providing the intended stability guarantees in active interaction. Instead, our design utilizes model-based external torque estimation, internal joint torque sensing, and redundant robot kinematics to actively support human physical input within the human interaction frequency bandwidth. We address practical challenges of relying on observer-based force estimation, including suppression of unmodeled joint elastic dynamic effects and measurement noise in the feedback path, reduced estimate accuracy close to kinematic singularities, and static gravity compensation errors. In a user study with 16 participants on a KUKA LWR iiwa we demonstrate statistically significant reductions in physical effort, improved maneuverability for both precise and agile tasks, and clear user preference.

发表机构

• Technology & Innovation Center, KUKA（库卡技术与创新中心）
• Institute of Robotics and Process Control, Technische Universität Braunschweig（布伦瑞克工业大学机器人与过程控制研究所）

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