ARIA Systems group develops novel theoretical foundations and computational frameworks to enable reliable and intelligent autonomy. The main theme of our work is safety and soundness, and the emphasis is on safe autonomy through correct-by-construction algorithmic approaches. Our research builds on knowledge developed in control theory, formal methods, statistical reasoning, and machine learning & AI to address real-world challenges in robotics and safety-critical systems.

Current Projects

Verifiable Control Synthesis through Model-based Learning with Safety Guarantees

We are combining formal control synthesis and machine learning to tackle uncertainty in autonomous systems applications. By carefully considering the error in the learning process, we aim to develop novel methods for safe control that can adapt to changes in the system and environment.

Explainable Multi-Agent Planning

As we begin employing teams of robots to accomplish complex tasks, it becomes difficult for a human to verify that future paths do not result in collisions. Therefore, we are working on new multiagent planning approaches that minimize the effort it takes a human to verify the paths.

INPASS: Intelligent Navigation, Planning, and Awareness for Swarm Systems

This project addresses the need to balance competing objectives for multi-agent autonomous systems in exploration of an environment. We aim to provide formal guarantees on performance and resource consumption objectives by using formal analysis techniques.

Correct-by-Construction Controller Synthesis using Gaussian Process Transfer Learning

This project explores improvements to embedded control software for safety-critical cyber-physical systems with applications in autonomous transportation, traffic networks, power networks, and aerospace systems. These systems often have complex dynamics that are difficult to obtain in a closed-form and ensure their safety.

Enabling Long-term Marine Robotic Autonomy, from Learning Specifications to Autonomous Navigation and Interaction

In this project, we explore the challenges of developing smart and safe long-term autonomy for underwater vehicles.

Past Work

Resource-performance trade-off for mobile robot design
Specification revision with optimal satisfaction guarantees
Decision making and Planning in partially unknown environments
Planning for probabilistic robots with complex missions
Automatic control synthesis from high-level specifications
Verification and synthesis for stochastic hybrid systems