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This paper introduces Anatomical Torque with Passivity-Based Control (ATP) for enhancing upper-limb exoskeleton assistance, addressing the challenges of providing responsive support for complex, nonperiodic movements. Utilizing a scalable musculoskeletal simulation framework, the authors trained a reinforcement-learning muscle controller that generates anatomical reference torques and refined these in real-time to adapt to diverse movements while ensuring safety through a learned anomaly score. Experimental results demonstrate that the ATP controller significantly reduces target-muscle activity by up to 48% compared to traditional methods, showcasing its effectiveness in both simulations and real-world applications.
Reducing target-muscle activity by up to 48% during dynamic tasks could revolutionize upper-limb exoskeleton design and user experience.
Providing assistance across diverse movements is a central objective of exoskeletons, and anatomical knowledge can enable responsive support that generalizes across tasks. However, anatomical assistance has mainly been studied for lower-limb exoskeletons, where periodic, weight-bearing motions impose lower demands on torque precision. Extending such assistance to complex, nonperiodic upper-limb movements remains challenging. This paper proposes Anatomical Torque with Passivity-Based Control (ATP) for safe upper-limb exoskeleton assistance. First, a scalable musculoskeletal simulation framework trains a unified reinforcement-learning muscle controller that generalizes across upper-limb movements and generates anatomical reference torques without complex biomechanical computations. Second, an online torque-refinement scheme adapts the reference to diverse movements, suppresses tendon-induced spikes, and incorporates a learned anomaly score for safe and comfortable assistance. Third, an interaction torque controller delivers assistance through a cable-driven compliant exoskeleton without constraining motion to predefined trajectories, while an energy tank preserves passivity with theoretical guarantees on torque tracking and system passivity. Simulations and real-world experiments show accurate tracking of long-duration motion sequences and generalization to real-time human movements. The controller achieves accurate torque tracking while preserving passivity and resumes tracking after energy-tank replenishment. An EMG study with five participants further shows reduced target-muscle activity during static and dynamic tasks compared with gravity compensation and open-loop assistance, with reductions of up to 48% relative to movement without the exoskeleton in a dynamic multi-joint task.
Tsinghua AI