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Conference paperLeonetti M, Ahmadzadeh SR, Kormushev P, 2013,
On-line Learning to Recover from Thruster Failures on Autonomous Underwater Vehicles
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Conference paperKormushev P, Caldwell DG, 2013,
Towards Improved AUV Control Through Learning of Periodic Signals
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BookDeisenroth MP, Neumann G, Peters J, 2013,
A Survey on Policy Search for Robotics
, Publisher: now PublishersPolicy search is a subfield in reinforcement learning which focuses onfinding good parameters for a given policy parametrization. It is wellsuited for robotics as it can cope with high-dimensional state and actionspaces, one of the main challenges in robot learning. We review recentsuccesses of both model-free and model-based policy search in robotlearning.Model-free policy search is a general approach to learn policiesbased on sampled trajectories. We classify model-free methods based ontheir policy evaluation strategy, policy update strategy, and explorationstrategy and present a unified view on existing algorithms. Learning apolicy is often easier than learning an accurate forward model, and,hence, model-free methods are more frequently used in practice. How-ever, for each sampled trajectory, it is necessary to interact with the robot, which can be time consuming and challenging in practice. Model-based policy search addresses this problem by first learning a simulatorof the robot’s dynamics from data. Subsequently, the simulator gen-erates trajectories that are used for policy learning. For both model-free and model-based policy search methods, we review their respectiveproperties and their applicability to robotic systems.
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Conference paperKormushev P, Caldwell DG, 2013,
Reinforcement Learning with Heterogeneous Policy Representations
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Conference paperKryczka P, Hashimoto K, Takanishi A, et al., 2013,
Walking Despite the Passive Compliance: Techniques for Using Conventional Pattern Generators to Control Instrinsically Compliant Humanoid Robots
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Conference paperCarrera A, Carreras M, Kormushev P, et al., 2013,
Towards valve turning with an AUV using Learning by Demonstration
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Conference paperKryczka P, Kormushev P, Hashimoto K, et al., 2013,
Hybrid gait pattern generator capable of rapid and dynamically consistent pattern regeneration
, Publisher: IEEE, Pages: 475-480 -
Journal articleFilippi S, Barnes CP, Cornebise J, et al., 2013,
On optimality of kernels for approximate Bayesian computation using sequential Monte Carlo
, STATISTICAL APPLICATIONS IN GENETICS AND MOLECULAR BIOLOGY, Vol: 12, ISSN: 2194-6302- Author Web Link
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- Citations: 50
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Conference paperKryczka P, Shiguematsu YM, Kormushev P, et al., 2013,
Towards dynamically consistent real-time gait pattern generation for full-size humanoid robots
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Journal articleKormushev P, Calinon S, Caldwell DG, 2013,
Reinforcement Learning in Robotics: Applications and Real-World Challenges
, Robotics, Vol: 2, Pages: 122-148, ISSN: 2218-6581
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