Multi-contact MPC for Dynamic Loco-manipulation on Humanoid Robots

Junheng Li, Quan Nguyen

This paper presents a novel method to control humanoid robot dynamic loco-manipulation with multiple contact modes via Multi-contact Model Predictive Control (MPC) framework. In this framework, we proposed a multi-contact dynamics model that can represent different contact modes in loco-manipulation (e.g., hand contact with object and foot contacts with ground). The proposed dynamics model simplifies the object dynamics as external force applied to the system (external force model) to ensure the simplicity and feasibility of the MPC problem. In numerical validations, our Multi-contact MPC framework only needs contact timings of each task and desired states to give MPC the knowledge of changes in contact modes in the prediction horizons in loco-manipulation. The proposed framework can control the humanoid robot to complete multi-tasks dynamic loco-manipulation applications such as efficiently picking up and dropping off objects while turning and walking.

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