Research
My work sits at the intersection of reinforcement learning, stochastic optimal control, game theory, and formal methods: turning high-level task specifications into robot policies that come with provable guarantees, and deploying those ideas on real systems — from humanoids to fleets of thousands of warehouse robots.
Projects
Learning-based Planning with Temporal Logic Constraints
Model-free reinforcement learning for stochastic planning under temporal logic constraints, using PCTL chance constraints and topological approximate dynamic programming.
Formal Methods and Game Theory for Cyber-Physical Security
Hypergame-theoretic framework for synthesizing deceptive strategies in adversarial environments, with applications to security in cyber-physical systems.
Anytime Planning via Function Approximation and Importance Sampling
Scalable anytime motion planning using function approximation and importance sampling, with support for parallel and cloud-accelerated computation.
DARPA Robotics Challenge
Motion planning and control for humanoid robots competing in the DARPA Robotics Challenge, focused on bipedal locomotion in disaster-response environments.
Funding
Research I have contributed to has been supported by:
