Constraint-aware learning of policies by demonstration

Leopoldo Armesto, João Moura, Vladimir Ivan, Mustafa Suphi Erden, Antonio Sala, Sethu Vijayakumar

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

Many practical tasks in robotic systems, such as cleaning windows, writing, or grasping, are inherently constrained. Learning policies subject to constraints is a challenging problem. In this paper, we propose a method of constraint-aware learning that solves the policy learning problem using redundant robots that execute a policy that is acting in the null space of a constraint. In particular, we are interested in generalizing learned null-space policies across constraints that were not known during the training. We split the combined problem of learning constraints and policies into two: first estimating the constraint, and then estimating a null-space policy using the remaining degrees of freedom. For a linear parametrization, we provide a closed-form solution of the problem. We also define a metric for comparing the similarity of estimated constraints, which is useful to pre-process the trajectories recorded in the demonstrations. We have validated our method by learning a wiping task from human demonstration on flat surfaces and reproducing it on an unknown curved surface using a force- or torque-based controller to achieve tool alignment. We show that, despite the differences between the training and validation scenarios, we learn a policy that still provides the desired wiping motion.
Original languageEnglish
Pages (from-to)1673-1689
Number of pages17
JournalInternational Journal of Robotics Research
Issue number13-14
Early online date26 Jul 2018
Publication statusPublished - 1 Dec 2018


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