Learning partial ordering constraints from a single demonstration

Anahita Mohseni-Kabir, Charles Rich, Sonia Chernova

Abstract: Current approaches to learning partial ordering constraints by demonstration require demonstrating all (or almost all) possible completion orders. We have developed an algorithm that, for plans involving relative placement of objects, learns the partial ordering constraints from a single demonstration by letting the user specify naturally conceived reference frame information. This work is an example of a broader research agenda that involves applying principles of human collaboration to robot learning from demonstration.