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
AirExo: Low-Cost Exoskeletons for Learning Whole-Arm Manipulation in the Wild
Hongjie Fang, Hao-Shu Fang, Yiming Wang, Jieji Ren, Jingjing Chen, Ruo Zhang, Weiming Wang, Cewu Lu
Learning to Assist Different Wearers in Multitasks: Efficient and Individualized Human-In-the-Loop Adaption Framework for Exoskeleton Robots
Yu Chen, Gong Chen, Jing Ye, Chenglong Fu, Bin Liang, Xiang Li
Advancements in Upper Body Exoskeleton: Implementing Active Gravity Compensation with a Feedforward Controller
Muhammad Ayaz Hussain, Ioannis Iossifidis
Intelligent upper-limb exoskeleton integrated with soft wearable bioelectronics and deep-learning for human intention-driven strength augmentation based on sensory feedback
Jinwoo Lee, Kangkyu Kwon, Ira Soltis, Jared Matthews, Yoonjae Lee, Hojoong Kim, Lissette Romero, Nathan Zavanelli, Youngjin Kwon, Shinjae Kwon, Jimin Lee, Yewon Na, Sung Hoon Lee, Ki Jun Yu, Minoru Shinohara, Frank L. Hammond, Woon-Hong Yeo