Last modified date: 04/08/2023
Scalable anytime motion planning using function approximation and importance sampling, with support for parallel and cloud-accelerated computation.
Motion planning and control for humanoid robots competing in the DARPA Robotics Challenge, focused on bipedal locomotion in disaster-response environments.
Hypergame-theoretic framework for synthesizing deceptive strategies in adversarial environments, with applications to security in cyber-physical systems.
Model-free reinforcement learning for stochastic planning under temporal logic constraints, using PCTL chance constraints and topological approximate dynamic programming.