A humanoid robot learns to play noughts and crosses and its harder variant with a few hundred images, a dozen dialogues, and simulated games, beating DQN baselines in simulation and playing with 130 humans in the wild.
Show, Attend and Interact: Perceivable Human-Robot Social Interaction through Neural Attention Q-Network
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abstract
For a safe, natural and effective human-robot social interaction, it is essential to develop a system that allows a robot to demonstrate the perceivable responsive behaviors to complex human behaviors. We introduce the Multimodal Deep Attention Recurrent Q-Network using which the robot exhibits human-like social interaction skills after 14 days of interacting with people in an uncontrolled real world. Each and every day during the 14 days, the system gathered robot interaction experiences with people through a hit-and-trial method and then trained the MDARQN on these experiences using end-to-end reinforcement learning approach. The results of interaction based learning indicate that the robot has learned to respond to complex human behaviors in a perceivable and socially acceptable manner.
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A Data-Efficient Deep Learning Approach for Deployable Multimodal Social Robots
A humanoid robot learns to play noughts and crosses and its harder variant with a few hundred images, a dozen dialogues, and simulated games, beating DQN baselines in simulation and playing with 130 humans in the wild.