Kavli Affiliate: David Muller
| First 5 Authors: Sammy Christen, David Müller, Agon Serifi, Ruben Grandia, Georg Wiedebach
| Summary:
Teleoperated robotic characters can perform expressive interactions with
humans, relying on the operators’ experience and social intuition. In this
work, we propose to create autonomous interactive robots, by training a model
to imitate operator data. Our model is trained on a dataset of human-robot
interactions, where an expert operator is asked to vary the interactions and
mood of the robot, while the operator commands as well as the pose of the human
and robot are recorded. Our approach learns to predict continuous operator
commands through a diffusion process and discrete commands through a
classifier, all unified within a single transformer architecture. We evaluate
the resulting model in simulation and with a user study on the real system. We
show that our method enables simple autonomous human-robot interactions that
are comparable to the expert-operator baseline, and that users can recognize
the different robot moods as generated by our model. Finally, we demonstrate a
zero-shot transfer of our model onto a different robotic platform with the same
operator interface.
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