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Active Incremental Learning of Robot Movement Primitives

Guilherme Maeda; Marco Ewerton; Takayuki Osa; Baptiste Busch; Jan Peters
In: Proceedings of the 1st Annual Conference on Robot Learning. Conference on Robot Learning (CoRL-2017), November 13-15, Mountain View, California, USA, Pages 37-46, Proceedings of Machine Learning Research, Vol. 78, PMLR, 2017.


Robots that can learn over time by interacting with non-technical users must be capable of acquiring new motor skills, incrementally. The problem then is deciding when to teach the robot a new skill or when to rely on the robot generalizing its actions. This decision can be made by the robot if it is provided with means to quantify the suitability of its own skill given an unseen task. To this end, we present an algorithm that allows a robot to make active requests to incrementally learn movement primitives. A movement primitive is learned on a trajectory output by a Gaussian Process. The latter is used as a library of demonstrations that can be extrapolated with confidence margins. This combination not only allows the robot to generalize using as few as a single demonstration but more importantly, to indicate when such generalization can be executed with confidence or not. In experiments, a real robot arm indicates to the user which demonstrations should be provided to increase its repertoire of reaching skills. Experiments will also show that the robot becomes confident in reaching objects for whose demonstrations were never provided, by incrementally learning from the neighboring demonstrations.

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