Robots that can be programmed by non-technical users must be capable of learning new tasks incrementally, via demonstrations. This poses the problem of selecting when to teach a new robot skill, or when to generalize a skill based on the current robot’s repertoire. Ideally, robots should actively make such decisions. The robot must quantify the suitability of its own skill set for a given query. It must reason whether it is confident enough to execute the task by itself, or if it should request a demonstration or corrections from a human.
We investigate algorithms for active requests for incremental learning of reaching skills via human demonstrations. Gaussian processes are used to extrapolate the current skill set with confidence margins, which are then encoded as movement primitives to accurately reach the desired query in the workspace of the robot. This combination allows the robot to generalize its primitives using as few as a single demonstration.
In the video below 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.
The contribution is reported in this paper
Maeda, G.; Ewerton, M.; Osa, T.; Busch, B. & Peters, J. “Active Incremental Learning of Robot Movement Primitives”. Proceedings of Machine Learning Research (PMLR) 1st Annual Conference on Robot Learning (CoRL), 2017, 78: Conference on Robot Learning (CoRL), 37-46. [pdf]