人間トルク推定に基づくタスク非依存型アシスト外骨格制御の一般的性能保証
General Performance Guarantee for Human Torque Estimation-Based Task-Agnostic Assistive Exoskeleton Control
人間のトルク推定誤差があっても、ロボットが意図に沿って動く確率の下限を理論的に保証する制御法を提案し、上肢外骨格で複数タスクの有効性を実証した。
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著者: Duy Hoang, Bastien Berret, Olivier Bruneau, Laurent Fribourg
分類: cs.RO, cs.LG, eess.SY
原文アブストラクト
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.
