  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 meta-level 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.
