Toward probabilistic safety bounds for robot learning from demonstration

摘要: Learning from demonstration is a popular method for teaching robots new skills. However, little work has looked at how to measure safety in the context of learning from demonstrations. We discuss three different types of safety problems that are important for robot learning from human demonstrations:(1) using demonstrations to evaluate the safety of a robot’s current policy,(2) using demonstrations to enable risk-aware policy improvement, and (3) determining when the demonstrations received by the robot are sufficient to ensure a desired safety level. We propose a risk-aware Bayesian sampling approach based on inverse reinforcement learning that provides a first step towards addressing these problems. We demonstrate the validity of our approach on a simulated navigation task and discuss promising areas for future work.