Human-robot cross-training: Computational formulation, modeling and evaluation of a human team training strategy
Author(s)Nikolaidis, Stefanos; Shah, Julie A.
MetadataShow full item record
We design and evaluate human-robot cross-training, a strategy widely used and validated for effective human team training. Cross-training is an interactive planning method in which a human and a robot iteratively switch roles to learn a shared plan for a collaborative task. We first present a computational formulation of the robot's interrole knowledge and show that it is quantitatively comparable to the human mental model. Based on this encoding, we formulate human-robot cross-training and evaluate it in human subject experiments (n = 36). We compare human-robot cross-training to standard reinforcement learning techniques, and show that cross-training provides statistically significant improvements in quantitative team performance measures. Additionally, significant differences emerge in the perceived robot performance and human trust. These results support the hypothesis that effective and fluent human-robot teaming may be best achieved by modeling effective practices for human teamwork.
DepartmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory; Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
2013 8th ACM/IEEE International Conference on Human-Robot Interaction (HRI)
Institute of Electrical and Electronics Engineers
Nikolaidis, Stefanos, and Julie Shah. “Human-robot cross-training: Computational formulation, modeling and evaluation of a human team training strategy.” In 2013 8th ACM/IEEE International Conference on Human-Robot Interaction (HRI), 33-40. Institute of Electrical and Electronics Engineers, 2013.
Author's final manuscript
INSPEC Accession Number: 13399442