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Bechtle, Sarah ; Molchanov, Artem ; Chebotar, Yevgen ; Grefenstette, Edward ; Righetti, Ludovic ; Sukhatme, Gaurav ; Meier, Franziska ( , 25th International Conference on Pattern Recognition)null (Ed.)
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Sutanto, Giovanni ; Rombach, Katharina ; Chebotar, Yevgen ; Su, Zhe ; Schaal, Stefan ; Sukhatme, Gaurav_S ; Meier, Franziska ( , The International Journal of Robotics Research)
Robots need to be able to adapt to unexpected changes in the environment such that they can autonomously succeed in their tasks. However, hand-designing feedback models for adaptation is tedious, if at all possible, making data-driven methods a promising alternative. In this paper, we introduce a full framework for learning feedback models for reactive motion planning. Our pipeline starts by segmenting demonstrations of a complete task into motion primitives via a semi-automated segmentation algorithm. Then, given additional demonstrations of successful adaptation behaviors, we learn initial feedback models through learning-from-demonstrations. In the final phase, a sample-efficient reinforcement learning algorithm fine-tunes these feedback models for novel task settings through few real system interactions. We evaluate our approach on a real anthropomorphic robot in learning a tactile feedback task.