Physical and Algorithmic Intelligence for Scalable and Reliable Autonomy

A grand engineering challenge is to develop intelligent teams of distributed, heterogeneous autonomous robots that rapidly enable situational awareness in mixed indoor/outdoor, cluttered, and highly unknown environments.  Such teams can transform emergency response, defense, inspection and maintenance, especially in off-the-grid operations where the robots must rely on robot-to-robot communication only.  Achieving this requires an interdisciplinary approach that integrates physical intelligence (adaptive actuation, sensing, computing, communication) and AI (resource-aware perception, learning, reasoning, acting), across the single- and multi-agent levels.  In this talk, I will present my lab’s physical AI efforts to enable scalability and reliability for control and distributed planning, via (i) novel mophable quadrotors that are agile, disturbance-resilient, and maneuverable to rapidly and reliably enable situational awareness even in cluttered spaces; (ii) one-shot, self-supervised learning algorithms that enable the robots to adapt on-the-fly to unknown disturbances and dynamics that compromise control accuracy and planning; and (iii) distributed optimization algorithms that enable the robots to scale planning, despite the low data rates of robot-to-robot communications.  Key in our approach is to treat the body of the systems —structure of the robot, in the single-agent level, and topology of the mesh network, in the multi-agent level— as actively adaptable to optimize performance, upon integration with resource- and performance-aware optimization algorithms for adaptive planning and control.  We build on tools of bandit learning, regret optimization (convex and submodular), adaptive nonlinear MPC, and submodular optimization.  I will present evaluations in quadrotor hardware and in large-scale simulations (>40 robots) that account for realistic data-rate limitations.  I will also discuss open challenges.