Expressive motion with dancers

In recent years, researchers have explored human body posture and motion to control robots in more natural ways. These interfaces require the ability to track the body movements of the user in three dimensions. Deploying motion capture systems for tracking tends to be costly and intrusive and requires a clear line of sight, making them ill adapted for applications that need fast deployment. In this article, we use consumer-grade armbands, capturing orientation information and muscle activity, to interact with a robotic system through a state machine controlled by a body motion classifier. To compensate for the low quality of the information of these sensors, and to allow a wider range of dynamic control, our approach relies on machine learning. We train our classifier directly on the user to recognize (within minutes) which physiological state his or her body motion expresses. We demonstrate that on top of guaranteeing faster field deployment, our algorithm performs better than all comparable algorithms, and we detail its configuration and the most significant features extracted. As the use of large groups of robots is growing, we postulate that their interaction with humans can be eased by our approach. We identified the key factors to stimulate engagement using our system on 27 participants, each creating his or her own set of expressive motions to control a swarm of desk robots. The resulting unique dataset is available online together with the classifier and the robot control scripts.

近年来，研究人员致力于探究以更自然的方式控制机器人的人体姿态与运动。此类界面需具备追踪用户三维运动的能力。部署运动捕捉系统进行追踪往往成本高昂且具有侵入性，且需要清晰的视线，使其难以适应需要快速部署的应用场景。在本文中，我们利用消费级臂环捕捉姿态信息和肌肉活动，通过由人体运动分类器控制的有限状态机与机器人系统进行交互。为了补偿这些传感器信息质量的低劣，并允许更广泛的动态控制范围，我们的方法依赖于机器学习。我们直接在用户身上训练分类器，以在数分钟内识别其身体运动所表达的生理状态。我们证明了我们的算法不仅保证了更快的现场部署，而且性能优于所有可比算法，并详细阐述了其配置和提取的最显著特征。随着大量机器人使用量的增长，我们提出，通过我们的方法可以简化机器人与人类之间的交互。我们在27名参与者中确定了刺激参与的关键因素，每位参与者都创建了自己的一套表达性动作来控制桌面机器人群体。由此产生的独特数据集以及分类器和机器人控制脚本已在线提供。