Korivand, S.; Jalili, N.; Gong, J. Inertia-Constrained Reinforcement Learning to Enhance Human Motor Control Modeling. Sensors2023, 23, 2698.
Korivand, S.; Jalili, N.; Gong, J. Inertia-Constrained Reinforcement Learning to Enhance Human Motor Control Modeling. Sensors 2023, 23, 2698.
Korivand, S.; Jalili, N.; Gong, J. Inertia-Constrained Reinforcement Learning to Enhance Human Motor Control Modeling. Sensors2023, 23, 2698.
Korivand, S.; Jalili, N.; Gong, J. Inertia-Constrained Reinforcement Learning to Enhance Human Motor Control Modeling. Sensors 2023, 23, 2698.
Abstract
Locomotor impairment is a high-prevalent and significant source of disability and significantly impacts a large population’s quality of life. Despite decades of research in human locomotion, the challenges of simulating human movement to study the features of musculoskeletal drivers and clinical conditions remain. Most recent efforts in utilizing reinforcement learning (RL) techniques are promising to simulate human locomotion and reveal musculoskeletal drives. However, these simulations often failed to mimic natural human locomotion because most reinforcement strategies have yet to consider any reference data regarding human movement. To address these challenges, in this study, we designed a reward function based on the trajectory optimization rewards (TOR), and bio-inspired rewards, which includes the rewards obtained from reference motion data captured by a single Intertial Moment Unit (IMU) sensor. The sensor was equipped on the participants’ pelvis to capture reference motion data. Also, we adapted the reward function by leveraging previous research in walking simulation for TOR. The experimental results showed that the simulated agents with the modified reward function performed better in mimicking the collected IMU data from participants, which means the simulated human locomotion was more realistic. Also, as this bio-inspired defined cost, IMU data enhanced the agent’s capacity to converge during the training process. As a result, the models’ convergence is faster than those developed without reference motion data. Consequently, human locomotion can be simulated more quicker and in a broader range of environments with a better simulation performance.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
