Learning Prioritized Control of Motor Primitives

TL;DR

This paper introduces a learning method for prioritized control of motor primitives in robotics, enabling simultaneous execution of sub-tasks with hierarchical override capabilities to improve task management.

Contribution

It presents a novel learning approach for prioritized control laws based on motor primitives, allowing effective task prioritization in robotic control systems.

Findings

Successful implementation on a ball bouncing task

Higher priority primitives override lower priority commands effectively

Demonstrates improved task management in robotic control

Abstract

Many tasks in robotics can be decomposed into sub-tasks that are performed simultaneously. In many cases, these sub-tasks cannot all be achieved jointly and a prioritization of such sub-tasks is required to resolve this issue. In this paper, we discuss a novel learning approach that allows to learn a prioritized control law built on a set of sub-tasks represented by motor primitives. The primitives are executed simultaneously but have different priorities. Primitives of higher priority can override the commands of the conflicting lower priority ones. The dominance structure of these primitives has a significant impact on the performance of the prioritized control law. We evaluate the proposed approach with a ball bouncing task on a Barrett WAM.