Intrinsically motivated reinforcement learning for human-robot interaction in the real-world

Abstract

For a natural social human-robot interaction, it is essential for a robot to learn the human-like social skills. However, learning such skills is notoriously hard due to the limited availability of direct instructions from people to teach a robot. In this paper, we propose an intrinsically motivated reinforcement learning framework in which an agent gets the intrinsic motivation-based rewards through the action-conditional predictive model. By using the proposed method, the robot learned the social skills from the human-robot interaction experiences gathered in the real uncontrolled environments. The results indicate that the robot not only acquired human-like social skills but also took more human-like decisions, on a test dataset, than a robot which received direct rewards for the task achievement.

arXiv comment34 pages

References in corpus (5)

• Continuous control with deep reinforcement learning
• End-to-End Training of Deep Visuomotor Policies
• Variational Information Maximisation for Intrinsically Motivated Reinforcement Learning
• Surprise-Based Intrinsic Motivation for Deep Reinforcement Learning
• Show, Attend and Interact: Perceivable Human-Robot Social Interaction through Neural Attention Q-Network

Cited by in corpus (6)

• The Conversation is the Command: Interacting with Real-World Autonomous Robot Through Natural Language
• Composing Task-Agnostic Policies with Deep Reinforcement Learning
• Adversarial Imitation via Variational Inverse Reinforcement Learning
• Boosting Deep Reinforcement Learning with Semantic Knowledge for Robotic Manipulators
• Analyzing the Hidden Activations of Deep Policy Networks: Why Representation Matters
• Stepwise Acquisition of Dialogue Act Through Human-Robot Interaction