Reinforcement Learning of Human Movement

Treadmills are widely used in rehabilitation and gait analysis. However, previous studies have reported differences in terms of kinematics and kinetics between treadmill and overground walking due to physical and psychological factors. The aim of this study was to analyze gait differences due to only the physical factors of treadmill walking. The study has been published in Frontiers in Bioengineering and Biotechnology. - by Minki Jung

A human model and its gait controller were created. A patient model was created by limiting the hip joint torque of the human model. A gait assistive device with four degrees of freedom was attached to the patient model. The controller of the device was trained using a reinforcement learning method to help the patient model walk normally. - by Jonghyun Park

The skeletal model has 31 DOFs including the six DOFs in the root body. Ninety-two Hill-type muscles were attached to the lower limbs following a previous study (Rajagopal et al., 2016). The gait controller could provide excitation signals for the muscles to make it walk after a reinforcement learning to follow a reference motion (Peng et al., 2018). - by Youngjun Koo