Multi-task policy search for robotics

Learning policies that generalize across multiple tasks is an important and challenging research topic in reinforcement learning and robotics. Training individual policies for every single potential task is often impractical, especially for continuous task variations, requiring more principled appro...

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Bibliographic Details
Main Authors: Deisenroth, Marc Peter, Englert, Peter, Peters, Jan, Fox, Dieter
Format: Conference Proceeding
Language:English
Subjects:
Approximation methods
Artificial neural networks
Cameras
Grippers
Robot kinematics
Training
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Summary:Learning policies that generalize across multiple tasks is an important and challenging research topic in reinforcement learning and robotics. Training individual policies for every single potential task is often impractical, especially for continuous task variations, requiring more principled approaches to share and transfer knowledge among similar tasks. We present a novel approach for learning a nonlinear feedback policy that generalizes across multiple tasks. The key idea is to define a parametrized policy as a function of both the state and the task, which allows learning a single policy that generalizes across multiple known and unknown tasks. Applications of our novel approach to reinforcement and imitation learning in real-robot experiments are shown.
ISSN:1050-4729
2577-087X
DOI:10.1109/ICRA.2014.6907421