The Ingredients for Efficient Robot Learning and Exploration
Abstract: In this talk, I will outline ingredients for enabling efficient robot learning. First, I will demonstrate how large vision-language models can enhance scene understanding and generalization, allowing robots to learn general rules from specific examples for handling everyday objects. Then, I will describe a policy learning method that leverages equivariance to significantly reduce the amount of training data needed for learning from human demonstrations. Moving beyond learning from demonstrations, we will explore how simulation can enable robots to learn autonomously.