Experiment-free exoskeleton assistance via learning in simulation.

Journal: Nature
PMID: 38867127

Abstract

Exoskeletons have enormous potential to improve human locomotive performance. However, their development and broad dissemination are limited by the requirement for lengthy human tests and handcrafted control laws. Here we show an experiment-free method to learn a versatile control policy in simulation. Our learning-in-simulation framework leverages dynamics-aware musculoskeletal and exoskeleton models and data-driven reinforcement learning to bridge the gap between simulation and reality without human experiments. The learned controller is deployed on a custom hip exoskeleton that automatically generates assistance across different activities with reduced metabolic rates by 24.3%, 13.1% and 15.4% for walking, running and stair climbing, respectively. Our framework may offer a generalizable and scalable strategy for the rapid development and widespread adoption of a variety of assistive robots for both able-bodied and mobility-impaired individuals.

Authors

  • Shuzhen Luo
    Lab of Biomechatronics and Intelligent Robotics, Department of Mechanical and Aerospace Engineering, North Carolina State University, Raleigh, NC, USA.
  • Menghan Jiang
    Lab of Biomechatronics and Intelligent Robotics, Department of Mechanical and Aerospace Engineering, North Carolina State University, Raleigh, NC, USA.
  • Sainan Zhang
    Lab of Biomechatronics and Intelligent Robotics, Department of Mechanical and Aerospace Engineering, North Carolina State University, Raleigh, NC, USA.
  • Junxi Zhu
    Lab of Biomechatronics and Intelligent Robotics, Department of Mechanical and Aerospace Engineering, North Carolina State University, Raleigh, NC, USA.
  • Shuangyue Yu
    Lab of Biomechatronics and Intelligent Robotics, Department of Mechanical and Aerospace Engineering, North Carolina State University, Raleigh, NC, USA.
  • Israel Dominguez Silva
    Lab of Biomechatronics and Intelligent Robotics, Department of Mechanical and Aerospace Engineering, North Carolina State University, Raleigh, NC, USA.
  • Tian Wang
    Department of Computer Science and Engineering, Huaqiao University, Xiamen, China.
  • Elliott Rouse
    Neurobionics Lab, Department of Robotics, Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI, USA.
  • Bolei Zhou
    Department of Computer Science, University of California, Los Angeles, CA, USA.
  • Hyunwoo Yuk
    Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology, Daejeon, South Korea.
  • Xianlian Zhou
    Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ, USA.
  • Hao Su
    1 Department of Urology, The Second Xiangya Hospital, Central South University, Changsha, Hunan 410011, China.

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