教机器人跨越功能性表达运动的空间
Teaching Robots to Span the Space of Functional Expressive Motion
摘要：
我们的目标是使机器人能够以情感的方式执行功能性任务，无论是响应用户的情感状态，还是表达他们的信心水平。先前的工作已经提出了学习独立的成本函数，从用户反馈的每个目标的情绪，使机器人可以优化它旁边的任务和环境特定的目标，它遇到的任何情况。然而，这种方法在建模多种情绪时效率低下，并且无法推广到新的情绪。在这项工作中，我们利用了这样一个事实，即情绪并不是相互独立的：它们是通过一个潜在的空间的价-唤醒-支配（VAD）。我们的关键思想是学习一个模型，用于如何将轨迹映射到带有用户标签的VAD上。考虑到轨迹映射和目标VAD之间的距离，允许这个单一模型表示所有情绪的成本函数。因此，1）所有用户反馈都可以有助于学习每种情绪; 2）机器人可以为空间中的任何情绪生成轨迹，而不仅仅是几个预定义的轨迹;以及3）机器人可以通过将用户生成的自然语言映射到目标VAD来对用户生成的自然语言做出情感响应。我们介绍了一种方法，交互式学习映射轨迹到这个潜在的空间，并在模拟和用户研究中进行测试。在实验中，我们使用了一个简单的真空机器人以及卡西机器人。
英文摘要：
Our goal is to enable robots to perform functional tasks in emotive ways, be it in response to their users' emotional states, or expressive of their confidence levels. Prior work has proposed learning independent cost functions from user feedback for each target emotion, so that the robot may optimize it alongside task and environment specific objectives for any situation it encounters. However, this approach is inefficient when modeling multiple emotions and unable to generalize to new ones. In this work, we leverage the fact that emotions are not independent of each other: they are related through a latent space of Valence-Arousal-Dominance (VAD). Our key idea is to learn a model for how trajectories map onto VAD with user labels. Considering the distance between a trajectory's mapping and a target VAD allows this single model to represent cost functions for all emotions. As a result 1) all user feedback can contribute to learning about every emotion; 2) the robot can generate trajectories for any emotion in the space instead of only a few predefined ones; and 3) the robot can respond emotively to user-generated natural language by mapping it to a target VAD. We introduce a method that interactively learns to map trajectories to this latent space and test it in simulation and in a user study. In experiments, we use a simple vacuum robot as well as the Cassie biped.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
