Perceptive Locomotion through Nonlinear Model Predictive Control

TL;DR

This paper introduces a real-time perception, planning, and control pipeline for quadruped robots that enables dynamic locomotion over rough terrain by optimizing foot placement and collision avoidance despite imperfect perceptive data.

Contribution

It presents a novel integrated approach combining convex terrain constraints with nonlinear model predictive control for robust, real-time quadruped locomotion in complex environments.

Findings

Achieved state-of-the-art dynamic climbing in simulation and on hardware.

Successfully navigated gaps, slopes, and stepping stones.

Validated real-time performance on the ANYmal quadruped platform.

Abstract

Dynamic locomotion in rough terrain requires accurate foot placement, collision avoidance, and planning of the underactuated dynamics of the system. Reliably optimizing for such motions and interactions in the presence of imperfect and often incomplete perceptive information is challenging. We present a complete perception, planning, and control pipeline, that can optimize motions for all degrees of freedom of the robot in real-time. To mitigate the numerical challenges posed by the terrain a sequence of convex inequality constraints is extracted as local approximations of foothold feasibility and embedded into an online model predictive controller. Steppability classification, plane segmentation, and a signed distance field are precomputed per elevation map to minimize the computational effort during the optimization. A combination of multiple-shooting, real-time iteration, and a filter-based line-search are used to solve the formulated problem reliably and at high rate. We validate the proposed method in scenarios with gaps, slopes, and stepping stones in simulation and experimentally on the ANYmal quadruped platform, resulting in state-of-the-art dynamic climbing.