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• W1529793276 abstract "There is currently a gap between the types of robot control polices that can be learnt from interactive demonstration and those manually programmed by skilled coders. Using regression-based robot tutelage, robots can be taught previously unknown tasks whose underlying mappings between perceived world states and actions are many-to-one. However, in a finite state machine controller, multiple subtasks may be applicable for a given perception, leading to perceptual aliasing. The underlying mapping may then be one-to-many. To learn such policies, users are typically required to provide additional information, such as a decomposition of the overall task into subtasks, and possibly indications of where transitions occur. We examine the limits of a regression-based approach for learning an FSM controller from demonstration of a basic robot soccer goal-scoring task. While the individual subtasks can be learnt, full FSM learning from this data will require techniques for model selection and data segmentation. We discuss one possibility for addressing these issues and how it may be combined with existing work." @default.
• W1529793276 created "2016-06-24" @default.
• W1529793276 creator A5071106238 @default.
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• W1529793276 date "2010-01-01" @default.
• W1529793276 modified "2023-09-27" @default.
• W1529793276 title "Can We Learn Finite State Machine Robot Controllers from Interactive Demonstration?" @default.
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• W1529793276 doi "https://doi.org/10.1007/978-3-642-05181-4_17" @default.
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