Wallbounce: Push Wall to Navigate with Contact-Implicit MPC
Xiaohan Liu, Cunxi Dai, John Z. Zhang, Arun L. Bishop, Zachary Manchester, Ralph Hollis
Abstract
In this work, we introduce a framework that enables highly maneuverable locomotion using non-periodic contacts. This task is challenging for traditional optimization and planning methods to handle due to difficulties in specifying contact mode sequences in real-time. To address this, we use a bi-level contact-implicit planner and hybrid model predictive controller to draft and execute a motion plan. We investigate how this method allows us to plan arm contact events on the shmoobot, a smaller ballbot, which uses an inverse mouseball drive to achieve dynamic balancing with a low number of actuators. Through multiple experiments we show how the arms allow for acceleration, deceleration and dynamic obstacle avoidance that are not achievable with the mouseball drive alone. This demonstrates how a holistic approach to locomotion can increase the control authority of unique robot morpohologies without additional hardware by leveraging robot arms that are typically used only for manipulation. Project website: https://cmushmoobot.github.io/Wallbounce
BibTeX
@inproceedings{icra2025_wallbouncepushwa,
title = {Wallbounce: Push Wall to Navigate with Contact-Implicit MPC},
author = {Xiaohan Liu and Cunxi Dai and John Z. Zhang and Arun L. Bishop and Zachary Manchester and Ralph Hollis},
booktitle = {ICRA 2025},
year = {2025}
}