Learning Relevant Features for Manipulation Skills using Meta-Level Priors

Abstract

Robots can generalize manipulation skills between different scenarios by adapting to the features of the objects being manipulated. Selecting the set of relevant features for generalizing skills has usually been performed manually by a human. Alternatively, a robot can learn to select relevant features autonomously. However, feature selection usually requires a large amount of training data, which would require many demonstrations. In order to learn the relevant features more efficiently, we propose using a meta-level prior to transfer the relevance of features from previously learned skills. The experiments show that the metalevel prior more than doubles the average precision and recall of the feature selection when compared to a standard uniform prior. The proposed approach was used to learn a variety of manipulation skills, including pushing, cutting, and pouring.