Gait pattern analysis for a weight carrying hexapod ant robot using reinforcement learning

Abstract

Robot hexapods are coming into focus in robotics fields due to their stable base and versatility in handling different terrains. The current study aims at identifying gait patterns to enhance efficiency and stability under dynamic payload conditions. It investigated six payload conditions: no payload, central payload, and four asymmetric payloads, which were front-left and back-left and front-right and back-right. To enable the dynamic modification of gait patterns, Proximal Policy Optimization (PPO), which is a type of reinforcement learning, was used in order to foster efficient and stable forward propulsion. To train and simulate a robot, Brax, an open-source physics simulation environment, was used under different payload conditions. It was shown that gait adaptation to loading distribution is achievable, while bilateral loading causes energy expenditures to grow. The research on hexapod movement is useful in the advancement of the field of bio-inspired robotics; it provides ideas for increasing hexapod mobility with unequal weight loadings and also helps to further extend hexapod robots’ usability.