Stable, Concurrent Controller Composition for Multi-Objective Robotic Tasks

TL;DR

This paper presents a stable, modular approach for combining multiple robotic control tasks using RMPflow, ensuring overall system stability through a CLF-based framework, validated by simulations and real robots.

Contribution

It extends RMPflow with a CLF-based stability analysis, enabling stable, concurrent controller composition for multi-objective robotic tasks.

Findings

RMPflow can stably combine subtask controllers satisfying CLF constraints.

The framework allows incorporation of design heuristics via nominal controllers.

Validation through numerical simulations and robotic experiments.

Abstract

Robotic systems often need to consider multiple tasks concurrently. This challenge calls for controller synthesis algorithms that fulfill multiple control specifications while maintaining the stability of the overall system. In this paper, we decompose multi-objective tasks into subtasks, where individual subtask controllers are designed independently and then combined to generate the overall control policy. In particular, we adopt Riemannian Motion Policies (RMPs), a recently proposed controller structure in robotics, and, RMPflow, its associated computational framework for combining RMP controllers. We re-establish and extend the stability results of RMPflow through a rigorous Control Lyapunov Function (CLF) treatment. We then show that RMPflow can stably combine individually designed subtask controllers that satisfy certain CLF constraints. This new insight leads to an efficient CLF-based computational framework to generate stable controllers that consider all the subtasks simultaneously. Compared with the original usage of RMPflow, our framework provides users the flexibility to incorporate design heuristics through nominal controllers for the subtasks. We validate the proposed computational framework through numerical simulation and robotic implementation.