Neural Style Transfer (NST) refers to a class of algorithms able to manipulate an element, most often images, to adopt the appearance or style of another one. Each element is defined as a combination of Content and Style: the Content can be conceptually defined as the what and the Style as the how of said element. In this context, we propose a custom NST framework for transferring a set of styles to the motion of a robotic manipulator, e.g., the same robotic task can be carried out in an angry, happy, calm, or sad way. An autoencoder architecture extracts and defines the Content and the Style of the target robot motions. A Twin Delayed Deep Deterministic Policy Gradient (TD3) network generates the robot control policy using the loss defined by the autoencoder. The proposed Neural Policy Style Transfer TD3 (NPST3) alters the robot motion by introducing the trained style. Such an approach can be implemented either offline, for carrying out autonomous robot motions in dynamic environments, or online, for adapting at runtime the style of a teleoperated robot. The considered styles can be learned online from human demonstrations. We carried out an evaluation with human subjects enrolling 73 volunteers, asking them to recognize the style behind some representative robotic motions. Results show a good recognition rate, proving that it is possible to convey different styles to a robot using this approach.

翻译：神经风格迁移（NST）指一类能够操纵元素（通常为图像）以呈现另一元素外观或风格的算法。每个元素可定义为内容与风格的组合：内容可概念性定义为"是什么"，风格则定义为"如何呈现"。在此背景下，我们提出一种定制化NST框架，用于将多种风格迁移至机器人机械臂的运动中——例如，同一机器人任务可分别以愤怒、愉悦、平静或悲伤的方式执行。自编码器架构提取并定义目标机器人运动的内容与风格；双延迟深度确定性策略梯度（TD3）网络利用自编码器定义的损失函数生成机器人控制策略。所提出的"神经策略风格迁移TD3"（NPST3）通过引入训练好的风格改变机器人运动。该方法既可用于离线场景——在动态环境中执行自主机器人运动，也可用于在线场景——在运行时调整遥操作机器人的风格。所考虑的各类风格可通过人类示范在线学习。我们招募73名志愿者进行人类受试者评估，要求其识别若干代表性机器人运动背后的风格。结果表明该方法具有较高的识别率，验证了通过此方法向机器人传递不同风格的可行性。