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- W4313005643 abstract "As robots become more prevalent in society, they will need to learn to act appropriately under diverse human teaching styles. We present a human-centered approach for teaching robots reward functions by using a mixture of teaching strategies when communicating action appropriateness and goal success. Our method incorporates two teaching strategies for learning: explicit action instruction and evaluative, scalar-based feedback. We demonstrate that a robot instantiating our method can learn from humans who use both kinds of strategies to train the robot in a complex navigation task that includes norm-like constraints." @default.
- W4313005643 created "2023-01-05" @default.
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- W4313005643 date "2022-03-07" @default.
- W4313005643 modified "2023-09-27" @default.
- W4313005643 title "Learning Reward Functions from a Combination of Demonstration and Evaluative Feedback" @default.
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- W4313005643 doi "https://doi.org/10.1109/hri53351.2022.9889498" @default.
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