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• W2600473424 abstract "Deep reinforcement learning is becoming increasingly popular for robot control algorithms, with the aim for a robot to self-learn useful feature representations from unstructured sensory input leading to the optimal actuation policy. In addition to sensors mounted on the robot, sensors might also be deployed in the environment, although these might need to be accessed via an unreliable wireless connection. In this paper, we demonstrate deep neural network architectures that are able to fuse information generated by multiple sensors and are robust to sensor failures at runtime. We evaluate our method on a search and pick task for a robot both in simulation and the real world." @default.
• W2600473424 created "2017-04-07" @default.
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• W2600473424 date "2017-09-01" @default.
• W2600473424 modified "2023-09-25" @default.
• W2600473424 title "Sensor fusion for robot control through deep reinforcement learning" @default.
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• W2600473424 doi "https://doi.org/10.1109/iros.2017.8206048" @default.
• W2600473424 hasPublicationYear "2017" @default.
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