Active Incremental Learning of a Contextual Skill Model Contextual skill models are learned to provide skills over a range of task parameters, often using regression across optimal taskspecific policies. However, the sequential nature of the learning process is usually neglected. In this paper, we propose to use active incremental learning by selecting a task which maximizes performance improvement over entire task set. The proposed framework exploits knowledge of individual tasks accumulated in a database and shares it among the tasks using a contextual skill model. The framework is agnostic to the type of policy representation, skill model, and policy search. We evaluated the skill improvement rate in two tasks, ballinacup and basketball. In both, active selection of tasks lead to a consistent improvement in skill performance over a baseline.

Incremental Estimation of Usersxe2x80x99 Expertise Level Estimating a userxe2x80x99s expertise level based on observations of their actions will result in better humanrobot collaboration, by enabling the robot to adjust its behaviour and the assistance it provides according to the skills of the particular user itxe2x80x99s interacting with. This paper details an approach to incrementally and continually estimate the expertise of a user whose goal is to optimally complete a given task. The userxe2x80x99s expertise level, here represented as a scalar parameter, is estimated by evaluating how far their actions are from optimal. The proposed approach was tested using data from an online study where participants were asked to complete various instances of a simulated kitting task. An optimal planner was used to estimate the xe2x80x9cgoodnessxe2x80x9d of all available actions at any given task state. We found that our expertise level estimates correlate strongly with observed aftertask performance metrics and that it is possible to differentiate novices from experts after observing, on average, 33% of the errors made by the novices.