Humanoid Loco-Manipulations Pattern Generation and Stabilization Control
🤖 AI Summary
Humanoid robots face significant challenges in maintaining dynamic balance and coordinating locomotion with manipulation when handling objects during walking, due to persistent or intermittent external contact forces—distinct from ground reaction forces. This paper proposes a dynamics-consistent locomotion-manipulation (locomanipulation) control framework. First, it embeds manipulation force reference trajectories directly into center-of-mass (CoM) motion planning; second, it designs a real-time feedback stabilizer based on external force tracking error. By integrating the linear inverted pendulum model with the divergent component of motion (DCM), the framework enables external-force-aware trajectory generation and robust gait adaptation. Evaluated in simulation and on a physical humanoid platform, the method significantly improves dynamic stability and trajectory tracking accuracy under complex manipulation tasks. It establishes a scalable theoretical and practical paradigm for unified locomotion-manipulation control.
Application Category
📝 Abstract
In order for a humanoid robot to perform loco-manipulation such as moving an object while walking, it is necessary to account for sustained or alternating external forces other than ground-feet reaction, resulting from humanoid-object contact interactions. In this letter, we propose a bipedal control strategy for humanoid loco-manipulation that can cope with such external forces. First, the basic formulas of the bipedal dynamics, i.e., linear inverted pendulum mode and divergent component of motion, are derived, taking into account the effects of external manipulation forces. Then, we propose a pattern generator to plan center of mass trajectories consistent with the reference trajectory of the manipulation forces, and a stabilizer to compensate for the error between desired and actual manipulation forces. The effectiveness of our controller is assessed both in simulation and loco-manipulation experiments with real humanoid robots.
Problem
Research questions and friction points this paper is trying to address.
Generating stable locomotion patterns under external manipulation forces
Compensating for errors between desired and actual manipulation forces
Ensuring humanoid robot stability during object manipulation while walking
Innovation
Methods, ideas, or system contributions that make the work stand out.
Bipedal control strategy for external forces
Pattern generator for CoM trajectories
Stabilizer compensates manipulation force errors
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