Maintaining Awareness of the Focus of Attention of a Conversation: A Robot-Centric Reinforcement Learning Approach
Marynel Vázquez (Disney Research Pittsburgh)
Aaron Steinfeld (Carnegie Mellon University
Scott E. Hudson (Carnegie Mellon University, Disney Research Pittsburgh)
International Symposium on Robot and Human Interactive Communication (RO-MAN) 2016
August 26, 2016
We explore online reinforcement learning techniques to find good policies to control the orientation of a mobile robot during social group conversations. In this scenario, we assume that the correct behavior for the robot should convey attentiveness to the focus of attention of the conversation. Thus, the robot should turn towards the speaker. Our results from tests in a simulated environment show that a new state representation that we designed for this problem can be used to find good policies for the robot. These policies can generalize across interactions with different numbers of people and can handle various levels of sensing noise.
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