Prioritized Optimal Control

TL;DR

This paper introduces a novel multi-task optimal control method that enforces strict task priorities in controlling redundant systems like humanoid robots, improving upon existing approaches by ensuring priority adherence and numerical stability.

Contribution

It proposes a new way to incorporate strict task priorities into optimal control, addressing limitations of weighting schemes and enhancing control accuracy for complex robots.

Findings

The method respects task priorities more reliably than traditional weighting approaches.

It demonstrates improved control performance in simulated humanoid robots.

The approach avoids numerical conditioning issues common in multi-task optimization.

Abstract

This paper presents a new technique to control highly redundant mechanical systems, such as humanoid robots. We take inspiration from two approaches. Prioritized control is a widespread multi-task technique in robotics and animation: tasks have strict priorities and they are satisfied only as long as they do not conflict with any higher-priority task. Optimal control instead formulates an optimization problem whose solution is either a feedback control policy or a feedforward trajectory of control inputs. We introduce strict priorities in multi-task optimal control problems, as an alternative to weighting task errors proportionally to their importance. This ensures the respect of the specified priorities, while avoiding numerical conditioning issues. We compared our approach with both prioritized control and optimal control with tests on a simulated robot with 11 degrees of freedom.