Safety-aware Goal-oriented Semantic Sensing, Communication, and Control for Robotics

TL;DR

This paper explores how to integrate safety considerations into goal-oriented semantic sensing, communication, and control for wirelessly-connected robotic systems, enhancing safety and task performance.

Contribution

It systematically analyzes safety and effectiveness challenges and proposes a co-design framework for safety-aware semantic sensing, communication, and control in robotic systems.

Findings

Semantic-based C&C packet execution improves safety rate by over 2 times.

Tracking success rate increases by more than 4.5 times with proposed methods.

Framework validated through UAV target tracking case study.

Abstract

Wirelessly-connected robotic systems empower robots with real-time intelligence by leveraging remote computing resources for decision-making. However, the data exchange between robots and edge servers often overwhelms communication links, introducing latency that degrades task performance. To tackle this, goal-oriented semantic communication (GSC) has been introduced for wirelessly-connected robotic systems to extract and transmit only goal-relevant semantic representations. While this improves task effectiveness, it generally overlooks practical safety requirements. Meanwhile, existing robotics research often treats safety primarily as a control-level problem, without systematically considering safety across sensing, communication, and control in a closed-loop manner. To bridge this gap, we investigate how to enable safety-aware goal-oriented semantic (SA-GS) sensing, communication, and control co-design in wirelessly-connected robotic systems, aiming to maximize the robotic task effectiveness subject to practical safety requirements. We first introduce {an} architecture {for} wirelessly-connected robotic systems and representative use cases. We then summarize general safety requirements and effectiveness metrics across the use cases. Next, we systematically analyze the unique safety and effectiveness challenges in sensing, communication, and control. Based on these, we further present potential SA-GS research directions. Finally, an Unmanned Aerial Vehicle (UAV) target tracking case study validates that one of the presented SA-GS research directions, i.e., semantic-based C\&C packet execution, could significantly improve safety rate and tracking success rate by more than 2 times and 4.5 times, respectively.