Perceptive Mixed-Integer Footstep Control for Underactuated Bipedal Walking on Rough Terrain

TL;DR

This paper presents a real-time perception and control system enabling underactuated bipeds to walk on rough, discontinuous terrain by optimizing footstep placement and stability using mixed-integer programming and advanced perception techniques.

Contribution

It introduces a novel model-predictive footstep control method combined with an online terrain decomposition approach for robust underactuated walking on challenging terrain.

Findings

Achieved state-of-the-art perceptive bipedal walking in outdoor experiments.

Developed a real-time terrain segmentation method using a single CPU thread.

Demonstrated stable walking on discontinuous terrain with the Cassie robot.

Abstract

Traversing rough terrain requires dynamic bipeds to stabilize themselves through foot placement without stepping in unsafe areas. Planning these footsteps online is challenging given non-convexity of the safe terrain, and imperfect perception and state estimation. This paper addresses these challenges with a full-stack perception and control system for achieving underactuated walking on discontinuous terrain. First, we develop model-predictive footstep control (MPFC), a single mixed-integer quadratic program which assumes a convex polygon terrain decomposition to optimize over discrete foothold choice, footstep position, ankle torque, template dynamics, and footstep timing at over 100 Hz. We then propose a novel approach for generating convex polygon terrain decompositions online. Our perception stack decouples safe-terrain classification from fitting planar polygons, generating a temporally consistent terrain segmentation in real time using a single CPU thread. We demonstrate the performance of our perception and control stack through outdoor experiments with the underactuated biped Cassie, achieving state of the art perceptive bipedal walking on discontinuous terrain. Supplemental Video: https://youtu.be/JK16KJXJxi4