Online Controller Synthesis for Robot Collision Avoidance: A Case Study

TL;DR

This paper introduces an online controller synthesis method for robot collision avoidance that incorporates uncertainty monitoring and repair of perception components, improving safety and reliability in dynamic environments.

Contribution

It presents a novel framework combining neural network monitoring, probabilistic model checking, and dual-component operation for robust, real-time robot collision avoidance.

Findings

Significant performance improvements over baseline methods.

Effective handling of perception uncertainty during operation.

Enhanced system availability during neural network repairs.

Abstract

The inherent uncertainty of dynamic environments poses significant challenges for modeling robot behavior, particularly in tasks such as collision avoidance. This paper presents an online controller synthesis framework tailored for robots equipped with deep learning-based perception components, with a focus on addressing distribution shifts. Our approach integrates periodic monitoring and repair mechanisms for the deep neural network perception component, followed by uncertainty reassessment. These uncertainty evaluations are injected into a parametric discrete-time markov chain, enabling the synthesis of robust controllers via probabilistic model checking. To ensure high system availability during the repair process, we propose a dual-component configuration that seamlessly transitions between operational states. Through a case study on robot collision avoidance, we demonstrate the efficacy of our method, showcasing substantial performance improvements over baseline approaches. This work provides a comprehensive and scalable solution for enhancing the safety and reliability of autonomous systems operating in uncertain environments.