Periodic Bipedal Gait Learning Using Reward Composition Based on a Novel Gait Planner for Humanoid Robots

This paper presents a periodic bipedal gait learning method using reward composition, integrated with a real-time gait planner for humanoid robots. First, we introduce a novel gait planner that incorporates dynamics to design the desired joint trajectory. In the gait design process, the 3D robot model is decoupled into two 2D models, which are then approximated as hybrid inverted pendulums (H-LIP) for trajectory planning. The gait planner operates in parallel in real time within the robot's learning environment. Second, based on this gait planner, we design three effective reward functions within a reinforcement learning framework, forming a reward composition to achieve periodic bipedal gait. This reward composition reduces the robot's learning time and enhances locomotion performance. Finally, a gait design example and performance comparison are presented to demonstrate the effectiveness of the proposed method.