Pablo Alves De Barros
ABSTRACT
Robots designed to collaborate with human partners should be able to implicitly anticipate and adapt to their needs. To do so, robots need a framework supporting cognition and mutual understanding in social settings. We posit that even basic cognitive processes, such as learning, could benefit from considering the social and affective dimensions. In this direction, we propose a recently developed scenario, based on a competitive game, as a tool to steer the development of socially-aware competitive reinforcement learning (RL).
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Index Terms
- Affect-Aware Learning for Social Robots
Index terms have been assigned to the content through auto-classification.
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