Data-Driven Imitation Learning for a Shopkeeper Robot with Periodically Changing Product Information
Keyword(s):
Data-driven imitation learning enables service robots to learn social interaction behaviors, but these systems cannot adapt after training to changes in the environment, such as changing products in a store. To solve this, a novel learning system that uses neural attention and approximate string matching to copy information from a product information database to its output is proposed. A camera shop interaction dataset was simulated for training/testing. The proposed system was found to outperform a baseline and a previous state of the art in an offline, human-judged evaluation.
2018 ◽
Vol 37
(13-14)
◽
pp. 1632-1672
◽
2019 ◽
Keyword(s):
2016 ◽
Vol 11
(10)
◽
pp. 65
◽