Signal Temporal Logic Compliant Co-design of Planning and Control

TL;DR

This paper introduces a novel co-design approach that combines trajectory planning and control using reinforcement learning and sampling-based methods to generate STL-compliant motion plans for autonomous robots.

Contribution

It presents a new model-free co-design strategy integrating motion primitives, reinforcement learning, and STL-based planning for autonomous robot tasks.

Findings

Successfully applied to differential-drive and quadruped robots

Generated feasible STL-compliant plans in various environments

Validated across multiple STL specifications

Abstract

This work presents a novel co-design strategy that integrates trajectory planning and control to handle STL-based tasks in autonomous robots. The method consists of two phases: $(i)$ learning spatio-temporal motion primitives to encapsulate the inherent robot-specific constraints and $(ii)$ constructing an STL-compliant motion plan from these primitives. Initially, we employ reinforcement learning to construct a library of control policies that perform trajectories described by the motion primitives. Then, we map motion primitives to spatio-temporal characteristics. Subsequently, we present a sampling-based STL-compliant motion planning strategy tailored to meet the STL specification. The proposed model-free approach, which generates feasible STL-compliant motion plans across various environments, is validated on differential-drive and quadruped robots across various STL specifications. Demonstration videos are available at https://tinyurl.com/m6zp7rsm.