  In this paper we address the problem of robot movement adaptation under
various environmental constraints interactively. Motion primitives are
generally adopted to generate target motion from demonstrations. However, their
generalization capability is weak while facing novel environments.
Additionally, traditional motion generation methods do not consider the
versatile constraints from various users, tasks, and environments. In this
work, we propose a co-active learning framework for learning to adapt robot
end-effector's movement for manipulation tasks. It is designed to adapt the
original imitation trajectories, which are learned from demonstrations, to
novel situations with various constraints. The framework also considers user's
feedback towards the adapted trajectories, and it learns to adapt movement
through human-in-the-loop interactions. The implemented system generalizes
trained motion primitives to various situations with different constraints
considering user preferences. Experiments on a humanoid platform validate the
effectiveness of our approach.
