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- W2892004125 abstract "Many rescue robots are reconfigurable, having subtracks (or flippers) that can be adjusted to help the robot traverse different types of terrain. Knowing how to adjust them requires skill on the part of the operator. If the robot is intended to run autonomously, the control system must have an understanding of how the flippers affect the robot’s interaction with the ground. We describe a system that first learns the effects of a robot’s actions and then uses this knowledge to plan how to reconfigure the robot’s tracks so that it can overcome different types of obstacles. The system is a hybrid of qualitative symbolic learning and reinforcement learning." @default.
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- W2892004125 date "2018-01-01" @default.
- W2892004125 modified "2023-10-05" @default.
- W2892004125 title "A Machine Learning System for Controlling a Rescue Robot" @default.
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- W2892004125 doi "https://doi.org/10.1007/978-3-030-00308-1_9" @default.
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