Abstract
In this article, an enactive architecture is described that allows a humanoid robot to learn to compose simple actions into turn-taking behaviours while playing interaction games with a human partner. The robot’s action choices are reinforced by social feedback from the human in the form of visual attention and measures of behavioural synchronisation. We demonstrate that the system can acquire and switch between behaviours learned through interaction based on social feedback from the human partner. The role of reinforcement based on a short-term memory of the interaction was experimentally investigated. Results indicate that feedback based only on the immediate experience was insufficient to learn longer, more complex turn-taking behaviours. Therefore, some history of the interaction must be considered in the acquisition of turn-taking, which can be efficiently handled through the use of short-term memory.
Original language | English |
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Article number | 26 |
Journal | Philosophies |
Volume | 4 |
Issue number | 2 |
DOIs | |
Publication status | Published - Jun 2019 |
Bibliographical note
Publisher Copyright:© 2019 by the authors. Licensee MDPI, Basel, Switzerland.
Funding
Funding: This research was mainly conducted within the EU Integrated Project RobotCub (Robotic Open-architecture Technology for Cognition, Understanding, and Behaviours) and was funded by the European Commission through the E5 Unit (Cognition) of FP6-IST under Contract FP6-004370. The work was also partially funded also by ITALK: Integration and Transfer of Action and Language Knowledge in Robots under Contract FP7-214668. These sources of support are gratefully acknowledged.
Funders | Funder number |
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FP6-IST | FP6-004370, FP7-214668 |
European Commission |
Keywords
- Developmental robotics
- Human–robot interaction
- Turn-taking