Name
Abstract | ||
|---|---|---|
Social robots use gestures to express internal and affective states, but their interactive capabilities are hindered by relying on preprogrammed or hand-animated behaviors, which can be repetitive and predictable. We propose a method for automatically synthesizing affective robot movements given manually-generated examples. Our approach is based on techniques adapted from deep learning, specifically generative adversarial neural networks (GANs).
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Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/HRI.2019.8673281 | 2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI) |
Keywords | Field | DocType |
Robots,Deep learning,Generators,Decoding,Neural networks,Generative adversarial networks,Motion measurement | Social robot,Computer science,Gesture,Human–computer interaction,Artificial intelligence,Generative grammar,Decoding methods,Deep learning,Artificial neural network,Affect (psychology),Robot | Conference |
ISSN | ISBN | Citations |
2167-2121 | 978-1-5386-8555-6 | 3 |
PageRank | References | Authors |
0.42 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Michael Suguitan | 1 | 4 | 2.48 |
| Mason Bretan | 2 | 18 | 2.28 |
| Guy Hoffman | 3 | 706 | 62.08 |