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| Content Provider | ACM Digital Library |
|---|---|
| Author | Mostafaoui, Ghilès Cuperlier, Nicolas Gaussier, Philippe Belkaid, Marwen Lesueur-Grand, Caroline |
| Abstract | Future robots are supposed to become our partners and share the environments where we live in our daily life. Considering the fact that they will have to co-exist with "nonexpert" people (elders, impaired people, children, etc.), we must rethink the way we design human/robot interactions. In this paper, we take a radical simplification route taking advantage from recent discoveries in low-level human interactions and dynamical motor control. Indeed, we argue for the need to take the dynamics of the interactions into account. Therefore, we propose a bio-inspired neuronal architecture to mimics adult/infant interactions that: (1) are initiated thanks to synchrony-based partner selection, (2) are maintained and re-engaged thanks to partner recognition and focus of attention, and (3) allow for learning sensorimotor navigation based on place/action associations. Our experiment shows good results for the learning of a navigation area and proves that this approach is promising for more complex tasks and interactions. |
| Starting Page | 1 |
| Ending Page | 5 |
| Page Count | 5 |
| File Format | |
| ISBN | 9781450342353 |
| DOI | 10.1145/2952744.2952754 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2016-07-13 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Sensorimotor navigation Partner selection Neural networks Human-robot interaction Synchrony |
| Content Type | Text |
| Resource Type | Article |
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