Prof. Williams' research concentrates on model based-autonomy -- the creation of long-lived autonomous systems that can explore, command, diagnose and repair themselves using fast, commonsense reasoning coupled to machine learning. Current research focuses on collaborative robotics and risk-bounded programming: Collaborative robotics extends model-based autonomy to robotic networks that cooperate under sea, on land, in the air and in space, on Earth and other planets. Risk-bounded programming embeds risk-sensitivity and commonsense within robotic explorers and everyday devices, by incorporating risk-bounded probabilistic reasoning within traditional embedded programming languages. Applications include annual robotic campaigns that explore deep sea volcanos in the Mediterranean and the artic ide sheet, robots that perform cooperative assembly, robots that assist at manipulation tasks, an autonomous air flight manager that interacts with the flight team and air traffic control, and a defensive autonomous driver that navigates Boston traffic.

Research interests include risk-bounded decision making and languages, collaborative and cooperative agents, tightly coupled task and motion planning, active learning and exploration based on semantic queries, and neural symbolic learning and reasoning.