Wallbounce : Push wall to navigate with Contact-Implicit MPC

TL;DR

This paper presents Wallbounce, a contact-implicit MPC framework enabling highly maneuverable robot locomotion through arm contact planning, demonstrated on a ballbot with arm-assisted dynamic balancing and obstacle avoidance.

Contribution

It introduces a bi-level contact-implicit planning and control approach that enhances robot maneuverability without extra hardware by leveraging existing robot arms.

Findings

Arm contact planning improves acceleration and deceleration capabilities.

The method enables dynamic obstacle avoidance.

Enhanced control authority with minimal hardware modifications.

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 mouse-ball 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 mouse-ball 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