[论文解读] I can attend a meeting too! Towards a human-like telepresence avatar robot to attend meeting on your behalf

Hrishav Bakul Barua, Chayan Sarkar+3 more|arXiv (Cornell University)|Jun 28, 2020

Speech and Audio Processing参考文献 13被引用 4

一句话总结

本文提出了一种类人远程呈现机器人，通过使用视听感知技术自动定位说话者并自然地转移注意力，代表远程用户自主参加线上会议。该系统结合了到达时间差（TDOA）进行声源定位与人脸及唇部运动检测，实现实时、上下文感知的机器人转向，从而提升用户在群体会议中的归属感和满意度。

ABSTRACT

Telepresence robots are used in various forms in various use-cases that helps to avoid physical human presence at the scene of action. In this work, we focus on a telepresence robot that can be used to attend a meeting remotely with a group of people. Unlike a one-to-one meeting, participants in a group meeting can be located at a different part of the room, especially in an informal setup. As a result, all of them may not be at the viewing angle of the robot, a.k.a. the remote participant. In such a case, to provide a better meeting experience, the robot should localize the speaker and bring the speaker at the center of the viewing angle. Though sound source localization can easily be done using a microphone-array, bringing the speaker or set of speakers at the viewing angle is not a trivial task. First of all, the robot should react only to a human voice, but not to the random noises. Secondly, if there are multiple speakers, to whom the robot should face or should it rotate continuously with every new speaker? Lastly, most robotic platforms are resource-constrained and to achieve a real-time response, i.e., avoiding network delay, all the algorithms should be implemented within the robot itself. This article presents a study and implementation of an attention shifting scheme in a telepresence meeting scenario which best suits the needs and expectations of the collocated and remote attendees. We define a policy to decide when a robot should rotate and how much based on real-time speaker localization. Using user satisfaction study, we show the efficacy and usability of our system in the meeting scenario. Moreover, our system can be easily adapted to other scenarios where multiple people are located.

研究动机与目标

• 使远程呈现机器人能够自主代表远程用户参加群体会议，最大限度减少人工干预。
• 解决多说话者会议环境中动态注意力转移的挑战，使机器人能够识别并聚焦于当前说话者。
• 通过类人注意力行为，提升远程和现场参与者对归属感和社交存在的感知。
• 设计一种适用于资源受限机器人平台（如TurtleBot2）的实时、本地化系统。
• 在参与者人数不同的真实会议场景中，评估系统的可用性与用户满意度。

提出的方法

• 利用基于Raspberry Pi的麦克风阵列实现时间差到达（TDOA）技术，实现实时声源定位（SSL）。
• 整合人脸检测与唇部运动检测，以验证和优化视觉注意力的说话者定位。
• 采用机器人状态表示模型，管理多人场景中在说话者之间的注意力切换。
• 应用决策规则，根据说话者活动与群体动态，确定何时以及以何种程度旋转机器人。
• 在TurtleBot2平台上实现整个系统，通过本地处理最大限度减少网络延迟，确保实时响应。
• 使用可调节的摄像头支架，优化坐姿会议配置下的面部检测效果。

实验结果

研究问题

• RQ1 如何使远程呈现机器人在动态群体会议环境中自主检测并聚焦于当前说话者？
• RQ2 何种注意力切换策略能在多说话者会议中最佳平衡响应速度、准确度与类人行为？
• RQ3 该机器人的自主行为在多大程度上增强了远程和现场参与者对归属感与满意度的感知？
• RQ4 在真实会议场景中，系统在注意力准确度与非必要转向方面的表现如何？
• RQ5 所提出的视听感知与状态模型能否在资源受限的机器人平台上实时有效部署？

主要发现

• 用户满意度较高，远程与现场参与者平均将系统带来的归属感评分评为10分制中的7分。
• 系统成功减少了不必要的转向与漏转，评估指标显示注意力切换的错误率较低。
• 说话者检测准确率高，机器人在大多数情况下能正确识别说话者，尤其在一对一及小型群体场景中表现优异。
• 在两人或以上群体中的注意力准确率保持稳定高位，机器人多数情况下能正确聚焦于活跃说话者。
• 视听感知系统使机器人能够以类人方式行为——在说话者之间自然切换注意力并维持眼神接触——而无需远程用户控制。
• 用户反馈证实，该机器人增强了远程参与者对个人存在感与社交包容感的体验，尤其在多人会议中效果显著。