Human-Inspired Gait and Jumping Motion Generation for Bipedal Robots Using Model Predictive Control

Abstract

In recent years, humanoid robot technology has been developing rapidly due to the need for robots to collaborate with humans or replace them in various tasks, requiring them to operate in complex human environments and placing high demands on their mobility. Developing humanoid robots with human-like walking and hopping abilities has become a key research focus, as these capabilities enable robots to move and perform tasks more efficiently in diverse and unpredictable environments, with significant applications in daily life, industrial operations, and disaster rescue. Currently, methods based on hybrid zero dynamics and reinforcement learning have been employed to enhance the walking and hopping capabilities of humanoid robots; however, model predictive control (MPC) presents two significant advantages: it can adapt to more complex task requirements and environmental conditions, and it allows for various walking and hopping patterns without extensive training and redesign. The objective of this study is to develop a bipedal robot controller using shooting method-based MPC to achieve human-like walking and hopping abilities, aiming to address the limitations of the existing methods and provide a new approach to enhancing robot mobility.