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.

本研究解决了自主交互机器人在表达性人机互动中的不足，提出了一种通过操作者数据进行模仿训练的模型。该模型能够在同一个变压器架构内学习连续和离散的操作者指令，实验结果表明，该方法实现的自主人机交互效果与专家基线相当，并且用户能够识别出机器人生成的不同情绪。

通过模仿操作者实现自主人机交互