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

Abstract

Traversing rough terrain requires dynamic bipeds to stabilize themselves through foot placement without stepping into unsafe areas. Planning these footsteps online is challenging given the nonconvexity of the safe terrain and imperfect perception and state estimation. This article 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, 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.