Adaptive CLF-MPC With Application To Quadrupedal Robots

TL;DR

This paper introduces an adaptive control framework combining Control Lyapunov Functions and Model Predictive Control to enhance the stability and performance of quadrupedal robots during complex tasks involving unknown payloads.

Contribution

It proposes a novel adaptive CLF-MPC approach that guarantees stability and improves performance in robotic manipulation and locomotion tasks.

Findings

Successful simulation validation on quadrupedal robots.

Hardware tests demonstrate improved stability with unknown payloads.

Enhanced interaction capabilities in real-world scenarios.

Abstract

Modern robotic systems are endowed with superior mobility and mechanical skills that make them suited to be employed in real-world scenarios, where interactions with heavy objects and precise manipulation capabilities are required. For instance, legged robots with high payload capacity can be used in disaster scenarios to remove dangerous material or carry injured people. It is thus essential to develop planning algorithms that can enable complex robots to perform motion and manipulation tasks accurately. In addition, online adaptation mechanisms with respect to new, unknown environments are needed. In this work, we impose that the optimal state-input trajectories generated by Model Predictive Control (MPC) satisfy the Lyapunov function criterion derived in adaptive control for robotic systems. As a result, we combine the stability guarantees provided by Control Lyapunov Functions (CLFs) and the optimality offered by MPC in a unified adaptive framework, yielding an improved performance during the robot's interaction with unknown objects. We validate the proposed approach in simulation and hardware tests on a quadrupedal robot carrying un-modeled payloads and pulling heavy boxes.