Safe model-based learning for robot control
Abstract:
In contrast to computers and smartphones, the promise of robotics is to design devices that can physically interact with the world. Envisioning robots to work in human-centered and interactive environments challenges current robot algorithm design, which has largely been based on a-priori knowledge about the system and its environment. In this talk, we will show how we combine models and data to achieve safe and high-performance robot behavior in the presence of uncertainties and unknown effects. In particular, we combine learned models in the form of Gaussian processes with classic tools from stability theory in order to analyze the stability of a controller on the learned model. Next, we combine this with model predictive control in order to obtain a control algorithm that is provably safe during the learning process. We demonstrate these algorithms on several experiments with self-driving vehicles. More information and videos at: www.dynsyslab.org and https://berkenkamp.me