Deep Reinforcement Learning based Personalized Locomotion Planning for Lower-Limb Exoskeletons

Abstract

This paper introduces intelligent central pattern generators (iCPGs) that can plan personalized walking trajectories for lower-limb exoskeletons. This can make walking more comfortable for the users by resolving one of the significant shortcomings of most commercially available exoskeletons, which is the use of pre-defined fixed trajectories for all users. The proposed method combines reinforcement learning (RL) with previously introduced adaptable central pattern generators (ACPGs) to learn a user's physical interaction behaviour and refine the exoskeleton's walking trajectories. The ACPG method embeds physical human-robot interaction (pHRI) in CPGs to make changing gait trajectories in real-time, possible. However, to effectively refine gait trajectories based on pHRIs, the parameters must be precisely identified and updated as a user interacts with the exoskeleton. Our proposed method uses RL to modify (amplify/attenuate) the pHRI energy based on a user's interaction behaviour, and form an effective energy value which can facilitate reaching desired gait pattern for users via iCPG dynamics. The proposed method can resolve the aforementioned challenges with ACPGs and personalized trajectory generation. The simulation and experimental results provide evidence that the proposed method can effectively adapt to the user's behaviour in different walking scenarios with the Indego lower-limb exoskeleton.