Autonomous Human-Robot Interaction via Operator Imitation
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Abstract (arXiv)
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.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 35 of its 50 references that are in the map. First posted on arXiv 3 Apr 2025.
Builds on
35 of 50 references.
References · 35
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23 more
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Citing papers per year: 2025: 2; 2026: 1.
3 papers.