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- W2220632146 abstract "We introduce a new method for robots to further improve upon skills acquired through Learning from Demonstration. Previously, we have introduced a method to learn both an action model to execute the skill and a goal model to monitor the execution of the skill. In this paper we show how to use the learned goal models to improve the learned action models autonomously, without further user interaction. Trajectories are sampled from the action model and executed on the robot. The goal model then labels them as success or failure and the successful ones are used to update the action model. We introduce an adaptive sampling method to speed up convergence. We show through both simulation and real robot experiments that our method can fix a failed action model." @default.
- W2220632146 created "2016-06-24" @default.
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- W2220632146 date "2015-09-01" @default.
- W2220632146 modified "2023-09-26" @default.
- W2220632146 title "Self-improvement of learned action models with learned goal models" @default.
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- W2220632146 doi "https://doi.org/10.1109/iros.2015.7354119" @default.
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