Safe, Robust and Adaptive Model-based Learning for Agile Robots: Autonomous Racing

Abstract

In recent years there has been a rapid development in agile robots capable of operating at their limits in dynamic environments. Autonomous racing and recent developments in it also spurred by competitions such as the Indy Autonomous Challenge, A2RL, and F1Tenth have shown how modern autonomous control algorithms are capable of operating racecars at their handling limits and also possibly capable of beating humans in the near future. What becomes challenging for such agile robots is their need to adapt rapidly to changing environmental conditions like surface change due to tire degradation, weather change, tire temperature changes in racing; and also the need to maintain a high control frequency necessary to execute such demanding tasks. The robots also need to stay safe while executing such tasks. In this work we show how we can leverage safe learning for agile robots operating at their limits, which safely enables them to learn and exploit their limits. We take autonomous racing as a case study, but will formulate an approach which can be applied generally to any agile control system.