A scalable and robust RL algorithm which optimizes for a combination of expected performance and tail risk under a distribution over learned reward functions.

An algorithm for safe reinforcement learning which utilizes a set of offline data to learn about constraints before policy learning and a pair of policies which seperate the often conflicting objectives of task directed exploration and constraint satisfaction to learn contact rich and visuomotor control tasks.

An MPC-based algorithm for robotic control (ABC-LMPC) with (1) performance and safety guarantees for stochastic nonlinear systems and (2) the ability to continuously explore the environment and expand the controller domain.

A new algorithm for safe and efficient reinforcement learning (SAVED) which leverages a small set of suboptimal demonstrations and prior task successes to structure exploration. SAVED also provides a mechanism for handling state-space constraints by leveraging probabilistic estimates of system dynamics.

A new formulation of imitiation learning from a non-stationary supervisor, associated theoretical analysis, and a practical algorithm to apply this formulation to develeop an RL algorithm which combines the sample efficiency of model-based RL and the fast policy evaluation enabled by model-free policies.