Uncertainty-Aware Imitation Learning using Kernelized Movement Primitives
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A4 Artikkeli konferenssijulkaisussa
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Date
2019
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en
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8
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Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2019, pp. 90-97, Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems
Abstract
During the past few years, probabilistic approaches to imitation learning have earned a relevant place in the robotics literature. One of their most prominent features is that, in addition to extracting a mean trajectory from task demonstrations, they provide a variance estimation. The intuitive meaning of this variance, however, changes across different techniques, indicating either variability or uncertainty. In this paper we leverage kernelized movement primitives (KMP) to provide a new perspective on imitation learning by predicting variability, correlations and uncertainty using a single model. This rich set of information is used in combination with the fusion of optimal controllers to learn robot actions from data, with two main advantages: i) robots become safe when uncertain about their actions and ii) they are able to leverage partial demonstrations, given as elementary sub-tasks, to optimally perform a higher level, more complex task. We showcase our approach in a painting task, where a human user and a KUKA robot collaborate to paint a wooden board. The task is divided into two sub-tasks and we show that the robot becomes compliant (hence safe) outside the training regions and executes the two sub-tasks with optimal gains otherwise.Description
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Silvério, J, Huang, Y, Abu-Dakka, F J, Rozo, L & Caldwell, D G 2019, Uncertainty-Aware Imitation Learning using Kernelized Movement Primitives. in Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2019. Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, IEEE, pp. 90-97, IEEE/RSJ International Conference on Intelligent Robots and Systems, Macau, China, 04/11/2019. https://doi.org/10.1109/IROS40897.2019.8967996