Learning control barrier functions (CBFs) for safe control of black-box systems. CBFs are a powerful tool to provide safety guarantee, but before this work, they cannot be directly applied to black box systems where their models are unavailable.

KETO is a framework for robots to manipulate unseen objects as tools to complete diverse tasks. We proposed a method to learn the keypoint representations of objects, which simplify the manipulation task and improve the generality to novel objects.

Reactive and safe agent modelings are important for nowadays traffic simulator designs and safe planning applications. We propose a control barrier function-based method to simulate traffic agents that behave like humans or human controlled vehicles, which react to other road participants.

We study constrained reinforcement learning (CRL) from a novel perspective by setting constraints directly on state density functions, rather than the value functions considered by previous work. State density has a clear physical and mathematical interpretation, and is able to express a wide variety of constraints such as resource limits and safety requirements.

Safety and stability are common requirements for robotic control systems. We propose a robust feedback method based on robust control Lyapunov barrier functions that generalize despite model uncertainty, and with safety and stability guarantee.

We address the problem of synthesizing provably correct controllers for linear systems with reach-avoid specifications. Our solution decomposes the overall synthesis problem into two smaller and more tractable problems, achieving a 2-150 times speedup compared with the previous techniques.