Research

Safety is crucial for deploying robots in the real world. One way of reasoning about safety of robots is by building safe sets through Hamilton-Jacobi (HJ) reachability. However, safe sets are often computed offline, assuming perfect knowledge of the dynamics, due to high compute time. In the presence of uncertainty, the safe set computed offline becomes inaccurate online, potentially leading to dangerous situations on the robot. We propose a novel framework to learn a safe control policy in simulation, and use it to generate online safe sets under uncertain dynamics. We start with a conservative safe set and update it online as we gather more information about our dynamics. We also show an application of our framework to a model-based reinforcement learning problem, proposing a safe model-based RL setup. Our framework enables robots to simultaneously learn about their dynamics, accomplish tasks, and update their safe sets. It also generalizes to complex high-dimensional dynamical systems, like 3-link manipulators and quadrotors, and reliably avoids obstacles, while achieving a task, even in the presence of unmodeled noise.