Locomotion on Constrained Footholds via Layered Architectures and Model Predictive Control

TL;DR

This paper presents a layered control architecture combining sampling-based discrete variable selection with smooth Model Predictive Control for real-time legged robot locomotion over complex terrains, improving optimality and reliability.

Contribution

It introduces a novel layered architecture that separates discrete and continuous control decisions, enabling real-time optimal control for legged robots navigating constrained environments.

Findings

More optimal and reliable than heuristic methods

Faster computation than pure sampling approaches

Successfully demonstrated on quadrupedal and humanoid robots

Abstract

Computing stabilizing and optimal control actions for legged locomotion in real time is difficult due to the nonlinear, hybrid, and high dimensional nature of these robots. The hybrid nature of the system introduces a combination of discrete and continuous variables which causes issues for numerical optimal control. To address these challenges, we propose a layered architecture that separates the choice of discrete variables and a smooth Model Predictive Controller (MPC). The layered formulation allows for online flexibility and optimality without sacrificing real-time performance through a combination of gradient-free and gradient-based methods. The architecture leverages a sampling-based method for determining discrete variables, and a classical smooth MPC formulation using these fixed discrete variables. We demonstrate the results on a quadrupedal robot stepping over gaps and onto terrain with varying heights. In simulation, we demonstrate the controller on a humanoid robot for gap traversal. The layered approach is shown to be more optimal and reliable than common heuristic-based approaches and faster to compute than pure sampling methods.