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Conference paperDallali H, Kormushev P, Tsagarakis N, et al., 2014,
Can Active Impedance Protect Robots from Landing Impact?
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Conference paperAhmadzadeh SR, Jamisola RS, Kormushev P, et al., 2014,
Learning Reactive Robot Behavior for Autonomous Valve Turning
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Conference paperJamisola RS, Kormushev P, Bicchi A, et al., 2014,
Haptic Exploration of Unknown Surfaces with Discontinuities
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Conference paperJamali N, Kormushev P, Caldwell DG, 2014,
Robot-Object Contact Perception using Symbolic Temporal Pattern Learning
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Conference paperAhmadzadeh SR, Carrera A, Leonetti M, et al., 2014,
Online Discovery of AUV Control Policies to Overcome Thruster Failures
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Conference paperCarrera A, Karras G, Bechlioulis C, et al., 2014,
Improving a Learning by Demonstration framework for Intervention AUVs by means of an UVMS controller
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Conference paperJamali N, Kormushev P, Ahmadzadeh SR, et al., 2014,
Covariance Analysis as a Measure of Policy Robustness in Reinforcement Learning
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Conference paperCarrera A, Palomeras N, Ribas D, et al., 2014,
An Intervention-AUV learns how to perform an underwater valve turning
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Journal articleDeisenroth MP, Fox D, Rasmussen CE, 2014,
Gaussian Processes for Data-Efficient Learning in Robotics and Control
, IEEE Transactions on Pattern Analysis and Machine Intelligence, ISSN: 0162-8828Autonomous learning has been a promising direction in control and robotics for more than a decade since data-drivenlearning allows to reduce the amount of engineering knowledge, which is otherwise required. However, autonomous reinforcementlearning (RL) approaches typically require many interactions with the system to learn controllers, which is a practical limitation in realsystems, such as robots, where many interactions can be impractical and time consuming. To address this problem, current learningapproaches typically require task-specific knowledge in form of expert demonstrations, realistic simulators, pre-shaped policies, orspecific knowledge about the underlying dynamics. In this article, we follow a different approach and speed up learning by extractingmore 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 modelerrors, a key problem in model-based learning. Compared to state-of-the art RL our model-based policy search method achieves anunprecedented speed of learning. We demonstrate its applicability to autonomous learning in real robot and control tasks.
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Journal articleLiepe J, Kirk P, Filippi S, et al., 2014,
A framework for parameter estimation and model selection from experimental data in systems biology using approximate Bayesian computation
, NATURE PROTOCOLS, Vol: 9, Pages: 439-456, ISSN: 1754-2189- Author Web Link
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- Citations: 132
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