Carnegie Mellon University
The
Human-robot mutual adaptation in collaborative tasks: Models and experiments

Stefanos Nikolaidis, David Hsu, and Siddhartha Srinivasa
The International Journal of Robotics Research, February, 2017.


Download
  • Adobe portable document format (pdf) (2MB)
Copyright notice: This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. These works may not be reposted without the explicit permission of the copyright holder.

Abstract
Adaptation is critical for effective team collaboration. This paper introduces a computational formalism for mutual adaptation between a robot and a human in collaborative tasks. We propose the Bounded-Memory Adaptation Model, which is a probabilistic finite-state controller that captures human adaptive behaviors under a bounded-memory assumption. We integrate the Bounded-Memory Adaptation Model into a probabilistic decision process, enabling the robot to guide adaptable participants towards a better way of completing the task. Human subject experiments suggest that the proposed formalism improves the effectiveness of human-robot teams in collaborative tasks, when compared with one-way adaptations of the robot to the human, while maintaining the human’s trust in the robot.

Notes

Text Reference
Stefanos Nikolaidis, David Hsu, and Siddhartha Srinivasa, "Human-robot mutual adaptation in collaborative tasks: Models and experiments," The International Journal of Robotics Research, February, 2017.

BibTeX Reference
@article{Nikolaidis_2017_8303,
   author = "Stefanos Nikolaidis and David Hsu and Siddhartha Srinivasa",
   editor = "SAGE",
   title = "Human-robot mutual adaptation in collaborative tasks: Models and experiments",
   journal = "The International Journal of Robotics Research",
   month = "February",
   year = "2017",
}