Neural Style Transfer (NST) was originally proposed to use feature extraction capabilities of Neural Networks as a way to perform Style Transfer with images. Pre-trained image classification architectures were selected for feature extraction, leading to new images showing the same content as the original but with a different style. In robotics, Style Transfer can be employed to transfer human motion styles to robot motions. The challenge lies in the lack of pre-trained classification architectures for robot motions that could be used for feature extraction. Neural Policy Style Transfer TD3 (NPST3) is proposed for the transfer of human motion styles to robot motions. This framework allows the same robot motion to be executed in different human-centered motion styles, such as in an angry, happy, calm, or sad fashion. The Twin Delayed Deep Deterministic Policy Gradient (TD3) network is introduced for the generation of control policies. An autoencoder network is in charge of feature extraction for the Style Transfer step. The Style Transfer step can be performed both offline and online: offline for the autonomous executions of human-style robot motions, and online for adapting at runtime the style of e.g., a teleoperated robot. The framework is tested using two different robotic platforms: a robotic manipulator designed for telemanipulation tasks, and a humanoid robot designed for social interaction. The proposed approach was evaluated for both platforms, performing a total of 147 questionnaires asking human subjects to recognize the human motion style transferred to the robot motion for a predefined set of actions.

翻译：神经风格迁移（NST）最初被提出，旨在利用神经网络的特征提取能力，实现图像领域的风格迁移。通过选取预训练的图像分类架构进行特征提取，可生成保留原始内容但具有不同风格的新图像。在机器人领域，风格迁移可用于将人类运动风格迁移至机器人运动。其挑战在于缺乏可用于特征提取的预训练机器人运动分类架构。本文提出神经策略风格迁移TD3（NPST3）框架，用于实现人类运动风格向机器人运动的迁移。该框架允许同一机器人运动以愤怒、快乐、平静或悲伤等不同人类情感风格执行。引入双延迟深度确定性策略梯度（TD3）网络生成控制策略，自编码器网络负责风格迁移步骤中的特征提取。风格迁移支持离线与在线两种模式：离线模式用于自主执行人类化风格机器人运动，在线模式可实时调整远程操控机器人的运动风格。该框架在两个不同机器人平台上进行测试：用于远程操控任务的机械臂，以及用于社交交互的人形机器人。针对两种平台，共开展147份问卷调查，要求受试者识别预设动作集中迁移至机器人运动的人类运动风格，以评估所提方法的有效性。