Exoskeleton Technology
Exoskeleton technology aims to augment human capabilities and assist individuals with motor impairments through wearable robotic systems. Current research emphasizes improving human-exoskeleton interaction, focusing on minimizing undesired forces through advanced modeling and control strategies, including impedance control, deep reinforcement learning (e.g., PPO with LSTM networks), and optimization algorithms (e.g., genetic algorithms, Big Bang-Big Crunch). These advancements are significant for rehabilitation, industrial applications, and exploration, offering potential for improved safety, efficiency, and quality of life.
Papers
Deep-Learning Control of Lower-Limb Exoskeletons via simplified Therapist Input
Lorenzo Vianello, Clément Lhoste, Emek Barış Küçüktabak, Matthew Short, Levi Hargrove, Jose L. Pons
Optimizing Locomotor Task Sets in Biological Joint Moment Estimation for Hip Exoskeleton Applications
Jimin An, Changseob Song, Eni Halilaj, Inseung Kang