Abstract:

In this talk I will retrace our research journey in the area of autonomous and intelligent systems. I will then present a high-level architecture for autonomy and dive into the technical details of a planning system we have implemented to bridge the gap between mission and motion level planning. In particular, I will discuss the problem of planning in a domain where the outcome of an action is probabilistic. Such planning problems are typically solved using Markov Decision Processes (MDP). To account for realistic mission requirements, we extend the MDP planning framework by accepting behavioral goals described in Linear Temporal Logic, and by incorporating constraints on critical resources or probability of mission success. The resulting planning problem can be solved via Constrained Markov Decision Processes. I will conclude the talk with a list of challenges that we consider still open in the area of autonomy for aerospace applications including explainability and assurance. 

Bio:

Dr. Pinto is a Technical Fellow at the United Technologies Research Center (UTRC). He works at the intersection of embedded system design, model-based design automation, and autonomous and intelligent systems. His current interests include architectural design for autonomous systems, knowledge representation, algorithms for high-level reasoning and decision making, safety assurance for autonomous systems, and compositional design methodologies. He is the recipient of the 2014 UTC Outstanding Achievement Award for his contributions in the area of Autonomous Rotorcraft Technology Development and Demonstration, and of the 2016 UTRC Technical Excellence Award. Dr. Pinto earned a Laurea degree in Electrical Engineering from the University of Rome “La Sapienza”, and a Ph.D. degree in Electrical Engineering and Computer Sciences from the University of California, Berkeley.