Learning and interacting in human-robot domains

  • Authors:

  • M. N. Nicolescu;M. J. Mataric

  • Affiliations:

  • Dept. of Comput. Sci., Univ. of Southern California, Los Angeles, CA;-

  • Venue:
  • IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
  • Year:
  • 2001

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Abstract

We focus on a robotic domain in which a human acts both as a teacher and a collaborator to a mobile robot. First, we present an approach that allows a robot to learn task representations from its own experiences of interacting with a human. While most approaches to learning from demonstration have focused on acquiring policies (i.e., collections of reactive rules), we demonstrate a mechanism that constructs high-level task representations based on the robot's underlying capabilities. Next, we describe a generalization of the framework to allow a robot to interact with humans in order to handle unexpected situations that can occur in its task execution. Without using explicit communication, the robot is able to engage a human to aid it during certain parts of task execution. We demonstrate our concepts with a mobile robot learning various tasks from a human and, when needed, interacting with a human to get help performing them