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Gaussian Processes for Data-Efficient Learning in Robotics and Control

Deisenroth, MP; Fox, D; Rasmussen, CE; (2015) Gaussian Processes for Data-Efficient Learning in Robotics and Control. IEEE Transactions on Pattern Analysis and Machine Intelligence , 37 (2) pp. 408-423. 10.1109/TPAMI.2013.218. Green open access

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Abstract

Autonomous learning has been a promising direction in control and robotics for more than a decade since data-driven learning allows to reduce the amount of engineering knowledge, which is otherwise required. However, autonomous reinforcement learning (RL) approaches typically require many interactions with the system to learn controllers, which is a practical limitation in real systems, such as robots, where many interactions can be impractical and time consuming. To address this problem, current learning approaches typically require task-specific knowledge in form of expert demonstrations, realistic simulators, pre-shaped policies, or specific knowledge about the underlying dynamics. In this paper, we follow a different approach and speed up learning by extracting more information from data. In particular, we learn a probabilistic, non-parametric Gaussian process transition model of the system. By explicitly incorporating model uncertainty into long-term planning and controller learning our approach reduces the effects of model errors, a key problem in model-based learning. Compared to state-of-the art RL our model-based policy search method achieves an unprecedented speed of learning. We demonstrate its applicability to autonomous learning in real robot and control tasks.

Type: Article
Title: Gaussian Processes for Data-Efficient Learning in Robotics and Control
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/TPAMI.2013.218
Publisher version: https://doi.org/10.1109/TPAMI.2013.218
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Computational modeling, Probabilistic logic, Approximation methods, Robots, Uncertainty, Data models, Predictive models, Policy search, robotics, control, Gaussian processes, Bayesian inference, reinforcement learning
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/10083554
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