A model is presented for analyzing the complexity of task assignment in a human/multi-robot team from the perspective of the human team member, or "operator". The focus is on complex domains where tasks have inter-dependencies and/or require tightly coupled coordination among robots. Preliminary validation of model metrics has been carried out through an experiment involving human subjects. Results suggest significant effect of key aspects of the model on human subjects' cognitive workload.

We present the initial stage of our research on Learning from Demonstration algorithms. We have implemented an algorithm based on Confident Execution, one of the components of the Confidence-Based Autonomy algorithm developed by Chernova and Veloso. Our preliminary experiments were conducted first in simulation and then using a Sony AIBO ERS-7 robot. So far, our robot has been able to learn crude navigation strategies, despite limited trials. We are currently working on improving our implementation by including additional features that describe more broadly the state of the agent. Our long term goal is to incorporate Learning from Demonstration techniques in our HRTeam (human/multi-robot) framework.

We present approaches to several fundamental tasks in multi-robot team-based exploration and localization, based on student projects developed in the past year.