Biomechanically-Inspired Bipedal Robot Locomotion via Hybrid Gait Representation and Model-Guided Reinforcement Learning

Abstract

Achieving stable and natural locomotion in bipedal robots, comparable to that of humans and animals, remains a long-standing challenge in robotics. In this work, we propose a bio-inspired low-level control framework that streamlines the generation of naturalistic gait patterns while ensuring adaptability. Our approach begins with the design of a low-dimensional gait representation that captures key characteristics of human and animal locomotion. This representation is then integrated with the Linear Inverted Pendulum Model (LIPM) to form an abstract yet effective motion descriptor. Serving as a kinematic reference within a reinforcement learning (RL) framework, this descriptor enables the training of control policies that strike a balance between biomechanical realism and adaptability. Rather than strictly adhering to predefined gait trajectories, the learned policies dynamically adjust to optimize both stability and velocity tracking. As a result, our method enables bipedal robots to exhibit smooth, biomechanically realistic locomotion while enhancing stability and adaptability. We validate the proposed framework through real-world experiments on our bipedal robot, demonstrating its ability to achieve stable and efficient locomotion.