用操作员数据训练机器人自主互动，能模仿不同情绪并跨平台迁移。
Autonomous Human-Robot Interaction via Operator Imitation
- 用扩散模型和分类器统一建模连续与离散指令，基于变压器架构学习操作员行为。
- 在仿真和真实系统中表现接近专家操作，用户可识别出不同情绪状态的机器人。
- 零样本迁移至新机器人平台，仅需相同操作界面即可运行。
遥控机器人角色可通过操作员的经验与社交直觉实现富有表现力的人机互动。本文提出一种方法，通过训练模型模仿操作员数据，实现自主交互机器人。模型基于人类-机器人互动数据集进行训练，其中专家操作员被要求改变机器人的互动方式与情绪，同时记录操作员指令以及人与机器人姿态。我们的方法利用扩散过程预测连续命令，通过分类器处理离散命令，全部整合在一个统一的Transformer架构中。我们在仿真环境和真实系统上进行了评估，并开展用户研究。结果表明，该方法可实现简单但媲美专家操作的自主人机互动，用户能有效识别由模型生成的不同机器人情绪。最后，我们展示了该模型在不同机器人平台上零样本迁移的成功应用，仅需相同的操作接口即可部署。
原文摘要 · Abstract (English)
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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