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- W2022647989 abstract "For robot application to proliferate in industry, and in unregulated environments, a simple means of programming is required. This paper describes methods for robot Learning from Demonstration (LfD). These methods used an RGB-D sensor for demonstration observation, and used finite state machines (FSMs) for policy derivation. Particularly, a method for object recognition was developed, which required only a single frame of data for training, and was able to perform real-time recognition. A planning method for object grasping was also developed. Experiments with a pick-and-place robot show that the developed methods resulted in object recognition accuracy greater than 99% in cluttered scenes, and manipulation accuracies of below 3mm in linear motion and 2° in rotation." @default.
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- W2022647989 date "2014-02-01" @default.
- W2022647989 modified "2023-09-27" @default.
- W2022647989 title "Robot Learning from Demonstration Using 3D Computer Vision" @default.
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- W2022647989 doi "https://doi.org/10.4028/www.scientific.net/amr.875-877.1994" @default.
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