Toward A Ballbot for Physically Leading People: A HumanCentered Approach This work presents a new humancentered method for indoor service robots to provide people with physical assistance and active guidance while traveling through congested and narrow spaces. As most previous work is robotcentered, this paper develops an endtoend framework which includes a feedback path of the measured human positions. The framework combines a planning algorithm and a humanrobot interaction module to guide the led person to a specified planned position. The approach is deployed on a personsize dynamically stable mobile robot, the CMU ballbot. Trials were conducted where the ballbot physically led a blindfolded person to safely navigate in a cluttered environment.

Follow The Robot: Modeling Coupled HumanRobot Dyads During Navigation Many robot applications being explored involve robots leading humans during navigation. Developing effective robots for this task requires a way for robots to understand and model a humanxe2x80x99s following behavior. In this paper, we present results from a user study of how humans follow a guide robot in the halls of an office building. We then present a datadriven Markovian model of this following behavior, and demonstrate its generalizability across time interval and trajectory length. Finally, we integrate the model into a global planner and run a simulation experiment to investigate the benefits of coupled humanrobot planning. Our results suggest that the proposed model effectively predicts how humans follow a robot, and that the coupled planner, while taking longer, leads the human significantly closer to the target position.