AI-Enhanced Robotic Exoskeletons Using Reinforcement Learning for Personalized Motion Assistance

Abstract

Robot exoskeletons have become an innovative technology helping people with mobility problems, enhancing rehabilitation, and boosting physical abilities of people. But standard exoskeletons are frequently constrained by fixed point controllers which are unable to adapt to user-specific gait patterns, physiological variations or dynamic environmental variations. Such inability to adapt leads to inefficiencies, discomfort and poor performance when performing locomotion tasks. Artificial intelligence, especially reinforcement learning (RL), is an influential method in recent years, which allows offering adaptive and user-oriented motion assistance. The RL agents are able to keep on learning with sensory feedback, modify control policies as well as optimize the assistance levels along with the changing human-robot interaction as dynamics. This flexibility enables exoskeletons to have a higher natural joint coordination, a greater stability and a personalized support when walking, climbing, or exercising.Reinforcement learning when combined with robotic exoskeletons also has a great potential to improve patient-specific outcomes in rehabilitation. The RL-based controllers are able to customize aid to aid in motor recovery, adjust the torque outputs based on the progress of the user, and respond to muscle fatigue dynamics in relation time. In addition, biomechanical sensing, wearable AI, and neuromuscular modeling used in synergy is useful in making more accurate predictions of user intent, which enable better coordination between human and machine. With an ever more integrated exoskeleton, in terms of multimodal sensors, e.g. electromyography (EMG), inertial measurement units (IMUs), force sensors, etc., RL algorithms can be trained to learn complex mapping between physiological signal and desired motion trajectory. The paper examines the conceptual and technical principles of AI-motion control exoskeletons that operate on reinforcement learning, discusses the current advances in the field, and suggests future developments in the methodology of building more customized, safer, and more user-friendly motion driver aid devices.