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We describe an algorithm for motion planning based on expert demonstrationsof a skill. In order to teach robots to perform complex object manipulationtasks that can generalize robustly to new environments, we must (1) learn arepresentation of the effects of a task and (2) find an optimal trajectory thatwill reproduce these effects in a new environment. We represent robot skills interms of a probability distribution over features learned from multiple expertdemonstrations. When utilizing a skill in a new environment, we compute featureexpectations over trajectory samples in order to stochastically optimize thelikelihood of a trajectory in the new environment. The purpose of this methodis to enable execution of complex tasks based on a library of probabilisticskill models. Motions can be combined to accomplish complex tasks in hybriddomains. Our approach is validated in a variety of case studies, including anAndroid game, simulated assembly task, and real robot experiment with a UR5.

Towards Robot Task Planning From Probabilistic Models of Human Skills