iLeAD: An EMG-Based Adaptive Shared Control Framework for Exoskeleton Assistance via Deep Reinforcement Learning

This paper introduces the Intelligent Learning Assistive Devices (iLeAD) framework, a shared control architecture for an elbow exoskeleton that adapts in real time to changing external conditions using deep reinforcement learning (RL). iLeAD employs a Latent Guidance Encoder (LGE) to encode shoulder configurations and external loads into latent variables, guiding the Exoskeleton Control Policy (ExoCoP) to provide adaptive torque assistance. An Online Latent Estimator (OLE), trained via knowledge distillation, enables the exoskeleton to continuously infer these latent factors from its own observations. To model human arm motion, a separate RL-based Musculoskeletal Control Policy (MusCoP) generates muscle activations, which we validate against static optimization (SO) and computed muscle control (CMC) in a high-fidelity musculoskeletal simulation. All experiments are performed in this simulation environment, demonstrating that iLeAD achieves precise elbow tracking and robust adaptation to dynamic loads and shoulder configurations. These results highlight a promising approach for intuitive, effective human-exoskeleton interaction and advance the potential for practical human power augmentation.