Prescribed Performance Control Guided Policy Improvement for Satisfying Signal Temporal Logic Tasks

TL;DR

This paper introduces a hybrid approach combining prescribed performance control with reinforcement learning to efficiently satisfy complex signal temporal logic tasks in robotic systems, demonstrated through simulated navigation tasks.

Contribution

It proposes a novel method that integrates PPC with reinforcement learning to improve exploration and satisfaction of STL tasks, balancing control guarantees and learning efficiency.

Findings

Effective in guiding exploration for complex STL tasks

Demonstrated success in simulated navigation scenarios

Balances control guarantees with learning flexibility

Abstract

Signal temporal logic (STL) provides a user-friendly interface for defining complex tasks for robotic systems. Recent efforts aim at designing control laws or using reinforcement learning methods to find policies which guarantee satisfaction of these tasks. While the former suffer from the trade-off between task specification and computational complexity, the latter encounter difficulties in exploration as the tasks become more complex and challenging to satisfy. This paper proposes to combine the benefits of the two approaches and use an efficient prescribed performance control (PPC) base law to guide exploration within the reinforcement learning algorithm. The potential of the method is demonstrated in a simulated environment through two sample navigational tasks.