Abstract: When deploying robots in “safety-critical” applications (e.g., medical robotics, self-driving cars, robot walking), control designers often seek to certify certain performance criteria (e.g, collision avoidance, remaining upright) for the closed-loop system. While there exist several “safe control” frameworks that can provide strong safety guarantees in theory, in practice these controllers are deployed in tandem with e.g., (vision-based) state estimators, in uncertain environments, that violate the assumptions underpinning the control design. An exciting (and open!) question is how to reason about the safety of the real-world systems under these uncertainties.
In this talk, I will present work which aims to bridge this gap between theory and practice at multiple points of the control hierarchy. First, I will discuss recent (lower-level) work which analyzes the safety of control barrier function (CBF)-based control architectures when deployed on systems subject to stochastic disturbances. Using techniques from martingale theory, we provide rigorous bounds on the finite-horizon safety probability of the system, and demonstrate these guarantees on problems including LQG control and an 18-DOF quadruped.
Then, moving to the higher level, I will present work that aims to guarantee the safety of a robot navigating an uncertain environment using only onboard vision. To do this, we represent the environment as a Neural Radiance Field (NeRF), a learning-based, uncertain scene representation that can be trained online using onboard vision. We show this scene representation provides rigorous (and natural) notions of collision probability, and use this collision probability to propose a chance-constrained path planner that can generate risk-sensitive trajectories through a NeRF scene.
Bio: Preston Culbertson is a postdoctoral scholar in the AMBER Lab at Caltech, working with Prof. Aaron Ames to research safe methods for robot planning and control using onboard vision. Preston completed his PhD at Stanford University, working under Prof. Mac Schwager, where his research focused on collaborative manipulation and assembly with teams of robots. In particular, Preston's research interests are in integrating modern techniques for computer vision-based state estimation with methods for robot control and planning that can provide safety guarantees. Preston received the NASA Space Technology Research Fellowship (NSTRF) as well as the "Best Manipulation Paper" award at ICRA 2018.