Pregled bibliografske jedinice broj: 1139753
Data-Driven Imitation Learning for a Shopkeeper Robot with Periodically Changing Product Information
Data-Driven Imitation Learning for a Shopkeeper Robot with Periodically Changing Product Information // ACM Transactions on Human-Robot Interaction, 10 (2021), 4; 31, 20 doi:10.1145/3451883 (međunarodna recenzija, članak, znanstveni)
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Naslov
Data-Driven Imitation Learning for a Shopkeeper Robot with Periodically Changing Product Information
Autori
Doering, Malcolm ; Brščić, Dražen ; Kanda, Takayuki
Izvornik
ACM Transactions on Human-Robot Interaction (2573-9522) 10
(2021), 4;
31, 20
Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni
Ključne riječi
learning from demonstrations ; HCI theory, concepts and models ; robotics ; question answering
Sažetak
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.
Izvorni jezik
Engleski
Znanstvena područja
Računarstvo
Citiraj ovu publikaciju:
Časopis indeksira:
- Web of Science Core Collection (WoSCC)
- Emerging Sources Citation Index (ESCI)
- Scopus
