Survey on safe robot control via learning

TL;DR

This survey reviews various approaches to safe robot control through learning, emphasizing methods that ensure safety constraints are met without compromising performance in complex environments.

Contribution

It provides a comprehensive overview of classical, learning-based, and embedded control techniques for safety in robotics, highlighting recent advancements and challenges.

Findings

Learning-based methods improve safety in robotic control

Classical control techniques are foundational but limited in adaptability

Embedded systems integrate safety constraints effectively

Abstract

Control systems are critical to modern technological infrastructure, spanning industries from aerospace to healthcare. This survey explores the landscape of safe robot learning, investigating methods that balance high-performance control with rigorous safety constraints. By examining classical control techniques, learning-based approaches, and embedded system design, the research seeks to understand how robotic systems can be developed to prevent hazardous states while maintaining optimal performance across complex operational environments.