Before revision:  In this work, given a reward function and a set of demonstrations from an expert that maximizes this reward function while respectingunknown constraints , we propose a framework to learn the most likely constraints that the expert respects.
After revision:  In this work, given a reward function and a set of demonstrations from an expert that maximizes this reward function while respecting constraints , we propose a framework to learn the most likely constraints that the expert respects.