发表机构
Université Paris-Saclay; CNRS; ENS Paris-Saclay; Inria（巴黎萨克雷大学; 法国国家科学研究中心; 巴黎萨克雷高等师范学校; 法国国家信息与自动化研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对外骨骼力矩估计误差导致辅助不匹配的问题，提出一种理论框架设计交互力矩，保证匹配辅助概率下界并泛化至未见数据，在ABLE上肢外骨骼上验证了多任务下的有效性能。
AI 中文摘要
准确的人体力矩估计对于实现机器人外骨骼系统中的任务无关控制至关重要。然而，估计误差可能导致机器人辅助与人类意图之间的不匹配，从而降低可控性和任务性能。在本文中，我们通过正式定义匹配辅助为机器人对人类运动产生积极贡献的场景来解决这一问题。基于这一定义，我们开发了一个理论框架来设计机器人的期望交互力矩，该框架保证了匹配辅助概率的下界。重要的是，所提出的保证适用于整个力矩分布，包括训练任务之外的未见数据。这为我们的方法提供了强大的可靠性和泛化能力，这两者对于有效的外骨骼控制都至关重要。所提出的策略在ABLE上肢外骨骼上实现，并在多任务设置中进行了评估。实验结果验证了理论保证，并表明所提出的策略在多个任务中实现了有效的总体性能，在保证运动平滑性的同时减少了人体的体力消耗。
英文摘要
Accurate human torque estimation is crucial for enabling task-agnostic control in robotic exoskeleton systems. However, estimation errors may cause mismatches between the robot assistance and the human intention, degrading controllability and task performance. In this paper, we address this issue by formally defining matched assistance as scenarios in which the robot positively contributes to human movement. Based on this definition, we develop a theoretical framework to design the robot's desired interaction torque that guarantees a lower bound on the matched assistance probability. Importantly, the proposed guarantee holds over the entire torque distribution, including unseen data beyond the training tasks. This provides our method with strong reliability and generalization, both of which are critical for effective exoskeleton control. The proposed strategy is implemented on the ABLE upper-limb exoskeleton and evaluated in a multi-task setup. Experimental results validate the theoretical guarantees and demonstrate that the proposed strategy achieves effective general performance across several tasks, guaranteeing movement smoothness while reducing human physical effort.
Comments10 pages, 8 figures
