Co-active Learning to Adapt Humanoid Movement for Manipulation

Title : Co-active Learning to Adapt Humanoid Movement for Manipulation
Authors :
Conference : 2016 IEEE-RAS International Conference on Humanoid Robots (Humanoids 2016) pp. 372-378
Date: November 15 - November 17, 2016

In this paper we address the problem of interactive robot movement adaptation under various environmental constraints. A common approach is to adopt motion primitives to generate target motions from demonstrations. However, their generalization capability is weak for novel environments. Additionally, traditional motion generation methods do not consider versatile constraints from different users, tasks, and environments. In this work, we propose a co-active learning framework for learning to adapt the movement of robot endeffectors for manipulation tasks. It is designed to adapt the original imitation trajectories, which are learned from demonstrations, to novel situations with different constraints. The framework also considers user feedback towards the adapted trajectories, and it learns to adapt movement through human-in-the-loop interactions. Experiments on a humanoid platform validate the effectiveness of our approach.

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