Squatting is one of the most demanding lower-limb movements, requiring substantial muscular effort and coordination. Reducing the physical demands of this task through intelligent and personalized assistance has significant implications, particularly in industries involving repetitive low-level assembly activities. In this study, we evaluated the effectiveness of a neural network controller for a modular Hip-Knee exoskeleton designed to assist squatting tasks. The neural network controller was trained via reinforcement learning (RL) in a physics-based, human-exoskeleton interaction simulation environment. The controller generated real-time hip and knee assistance torques based on recent joint-angle and velocity histories. Five healthy adults performed three-minute metronome-guided squats under three conditions: (1) no exoskeleton (No-Exo), (2) exoskeleton with Zero-Torque, and (3) exoskeleton with active assistance (Assistance). Physiological effort was assessed using indirect calorimetry and heart rate monitoring, alongside concurrent kinematic data collection. Results show that the RL-based controller adapts to individuals by producing torque profiles tailored to each subject's kinematics and timing. Compared with the Zero-Torque and No-Exo condition, active assistance reduced the net metabolic rate by approximately 10%, with minor reductions observed in heart rate. However, assisted trials also exhibited reduced squat depth, reflected by smaller hip and knee flexion. These preliminary findings suggest that the proposed controller can effectively lower physiological effort during repetitive squatting, motivating further improvements in both hardware design and control strategies.

翻译：深蹲是最具挑战性的下肢动作之一，需要大量的肌肉力量与协调性。通过智能化和个性化的辅助来降低该任务的体力消耗具有重要意义，尤其是在涉及重复性低强度装配作业的工业领域。本研究评估了一种用于模块化髋-膝外骨骼的神经网络控制器在辅助深蹲任务中的有效性。该神经网络控制器在基于物理的人机交互仿真环境中通过强化学习进行训练。控制器根据实时的关节角度与速度历史数据生成髋关节与膝关节的辅助力矩。五名健康成年人在三种条件下执行三分钟节拍器引导的深蹲：(1) 无外骨骼，(2) 外骨骼零力矩模式，以及(3) 外骨骼主动辅助模式。通过间接测热法与心率监测评估生理负荷，并同步采集运动学数据。结果表明，基于强化学习的控制器能适应个体差异，根据受试者的运动特征与节奏生成定制化的力矩曲线。与零力矩模式及无外骨骼条件相比，主动辅助使净代谢率降低约10%，心率亦有轻微下降。然而，辅助试验也表现出深蹲深度减小，体现为髋关节与膝关节屈曲角度降低。这些初步发现表明，所提出的控制器能有效降低重复深蹲过程中的生理负荷，为硬件设计与控制策略的进一步改进提供了动力。