Active Incremental Learning of Robot Movement Primitives
Robots must be capable of learning new tasks incrementally, via demonstrations. The problem then is to decide when the user should teach the robot a new skill, or when to trust the robot generalizing its own actions. In this paper, we propose a method where the robot actively make such decisions by quantifying the suitability of its own skill set for a given query via Gaussian Processes.
In this video you can see a robot indicating to the user which demonstrations should be provided to increase its repertoire of skills. The experiment also shows that the robot becomes confident in reaching objects for whose demonstrations were never provided, by incrementally learning from the neighboring demonstrations.