Safeguarding and Empowering General Purpose Robots through Abstraction and Constraint Certification

 Robots are increasingly deployed across various domains, from industrial automation to domestic assistance. Ensuring that robots operate safely and intelligently is crucial to preventing potential risks such as injury, loss of life, and economic costs. This thesis addresses key challenges in deploying robots in complex real-world environments, including providing formal safety guarantees in uncertain conditions, scaling safety guarantees to realistic high-dimensional systems, allowing the robot to behave intelligently while remaining explainable and trustworthy, and ensuring the robustness of neural network components. 

This thesis introduces a suite of tools to tackle these challenges. The first tool, Meta-Control, synthesizes heterogeneous robot skills with a hiearchical control approach, which could decompose system-level safety requirements into module-level constraints. These constraints are categorized into control and neural network constraints. For control constraints, the toolset introduces Abstract Safe Control for hierarchical safety guarantees, Robust Safe Control for handling model uncertainty through a control-limits aware robust framework, Neural Network Dynamic Models (NNDM) Safe Control for integrating data-driven models with safety guarantees, and Benchmark of Interactive Safety for benchmarking and unifying different safe control algorithms. For neural network constraints, the toolset introduces ModelVerification.jl toolbox for verifying neural network safety specifications, online verification for online assurance under domain shifts and network update, and the Signal-to-Noise Ratio (SNR) loss method to enhance stability and robustness of neural networks. 

These tools enable the provision of formal safety guarantees with partially known or unknown dynamic models in uncertain, interactive environments, achieving state-of-the-art control safety and neural network safety. This allows robot arms to perform various tasks efficiently and safely, advancing the development of reliable and trustworthy general-purpose robots.