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Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
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Affect-Driven Learning of Robot Behaviour for Collaborative Human-Robot Interactions.

Nikhil Churamani1, Pablo Barros2, Hatice Gunes1

  • 1Department of Computer Science and Technology, University of Cambridge, Cambridge, United Kingdom.

Frontiers in Robotics and AI
|March 10, 2022
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Summary
This summary is machine-generated.

This study introduces a new framework for social robots to understand and adapt to human emotions. Robots with a "patient" core were perceived as more persistent, while "impatient" robots were seen as more generous.

Keywords:
core affecthuman-robot interactionmulti-modal affect perceptionneural networksreinforcement learning

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Area of Science:

  • Human-Robot Interaction
  • Affective Computing
  • Artificial Intelligence

Background:

  • Current social robots lack dynamic affective adaptation.
  • Existing models map expressions to static robot actions, ignoring interactional context.

Purpose of the Study:

  • To propose a novel framework for affect-driven behavior generation in social robots.
  • To enable robots to dynamically adapt to users' affective states.

Main Methods:

  • A hybrid neural model for evaluating user expressions and speech.
  • An Affective Core using self-organizing neural models for behavioral traits (e.g., patience).
  • Reinforcement Learning to generate robot behaviors based on affective appraisal.

Main Results:

  • The Affective Core's dispositions significantly influenced the robot's affective appraisal.
  • User study (n=31) in the Ultimatum Game showed distinct negotiation strategies.
  • Patient robots with high emotional actuation were perceived as more persistent.

Conclusions:

  • The proposed framework enables robots to generate context-aware affective behaviors.
  • Modulating the Affective Core's traits influences robot behavior and user perception.
  • This research advances social robot adaptability in collaborative tasks.