Data-Driven Imitation Learning for a Shopkeeper Robot with Periodically Changing Product Information
Bibliographic Data
| ID | 22190682 |
|---|---|
| Authors | Malcolm Doering (0000-0002-2805-0187, Kyoto University), Dražen Brščić (0000-0001-8477-6460, Kyoto University), Takayuki Kanda (0000-0002-9546-5825, Kyoto University) |
| Year | 2021 |
| Volume | 10 |
| Issue | 4 |
| Pages | 1-20 |
| Publication date | 2021-12-31 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | ACM Transactions on Human-Robot Interaction (JOURNAL) |
| Journal identifiers | ISSN: 2573-9522 • E-ISSN: 2573-9522 |
| Publisher | Association for Computing Machinery (ACM) (PUBLISHER) |
| DOI | 10.1145/3451883 |
| OpenAlex | W3180340319 |
| Language | EN |
| Citations received | 1 |
| References cited | 32 |
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
Human–computer interaction · Imitation · Machine learning · Robot · Computer Science · Reinforcement Learning in Robotics · Robot Manipulation and Learning · Social Robot Interaction and HRI · Artificial Intelligence
| Citations per year | 1 |
|---|---|
| Citation span | 2026 - 2026 (1) |
| Highly cited | No |