Online Controller Synthesis for Robot Collision Avoidance: A Case Study

2025-02-08

AI Summary

This paper addresses the obstacle-avoidance control challenge for robots equipped with deep perception modules operating in dynamic, uncertain environments under distributional shift. We propose an online controller synthesis framework featuring a novel perception-module monitoring and repair mechanism—operating periodically—and integrating uncertainty re-evaluation with a seamless dual-controller switching architecture. Our approach unifies deep learning–based perception, parametric discrete-time Markov chain modeling, probabilistic model checking, and online controller synthesis. The key contribution is the first realization of real-time detection, quantification, and adaptive response to distributional shift within the perception–control closed loop, significantly enhancing system safety, robustness, and real-time performance. In standard obstacle-avoidance benchmarks, our method achieves a 23.6% improvement in safety rate and a 41% reduction in average response latency compared to baseline approaches, demonstrating effective multi-objective co-optimization.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Natural Language Processing: Safety and Robustness
• Planning, Routing, and Scheduling: Replanning and Plan Repair

Application Category

• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

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.

Problem

• Online controller synthesis
• Robot collision avoidance
• Deep learning-based perception

Innovation

• Online controller synthesis framework
• Deep neural network repair mechanisms
• Probabilistic model checking controllers