CHS: Small: Collaborative Research: Increasing Social Connectedness in Telerobotic Platforms Through Adding Gesture Capabilities

该项目将研究如何使常见的远程呈现机器人更具表现力和互动性，从而影响人们使用它们的意愿以及他们对远程合作者的看法。许多远程呈现机器人在移动平台上采用屏幕的形式，与普通的视频会议相比，为远程与会者提供了一个物理身体，可以增加临场感和互动性。然而，这些平台有限的非语言表达能力是它们使用的主要障碍。因此，研究目标是通过增加手和手臂来支持常见的非语言互动，如指向、手势和触摸，来增加这些平台的体现和社交互动能力。为了做到这一点，研究人员将首先建立一个简单的手和手臂来支持这些非语言互动，并将其添加到现有的远程呈现机器人中。然后，他们将开发在机器人上运行的软件，以执行手势并确保附近人员的安全，以及一个用户界面，将远程用户的手势映射到手和手臂能够执行的手势上。然后，他们将通过一对一和小组破冰对话的研究来测试该系统的可用性及其对社会存在的影响。这项工作将为远程呈现机器人带来更自然的界面，并更好地理解人们如何接受它们并与它们互动。这反过来又会带来社会效益，使远程交互更有效，节省时间、精力和旅行所需的燃料成本，同时不仅支持远程会议，还支持其他远程服务，如医疗诊断和护理。该团队还将把这项研究用于他们自己的课堂，并在旨在鼓励孩子们探索科学事业的活动中进行推广。这项工作是远程机器人、触觉学和社会心理学的交叉领域。由于手势、指向、握手和其他非语言交流是当前远程呈现平台不支持的人类互动的重要组成部分，因此工作重点是开发一种轻量级手臂，可以实现这些手势，而不像完全模仿人类运动的手臂那样复杂、脆弱和昂贵。为了做到这一点，研究团队将开发一种3d打印的5指手，具有3个自由度和简单的连接，使手指能够自然弯曲，以重现预期的手势。控制软件将使用正运动学和逆运动学方法来模拟手的配置，并使用开环控制器与人类操作员一起实现手势；碰撞、力和光学传感器将用于解决安全问题。对于人类远程操作员，该团队将开发接口(a)为机器人添加摄像头，以提供有效手势所需的远程环境的更全面视图，以及(b)使用运动跟踪硬件来检测远程用户的手臂运动并将其转化为机器人手臂可能的运动空间，专注于特定的目标社交手势。他们将通过一系列受试者之间的用户研究来评估该系统，让参与者作为远程或本地用户与有或没有手臂的机器人版本进行交互。参与者将与训练有素的实验伙伴互动，以减少可变性，并确保在有手臂和没有手臂的情况下都能体验到目标非语言手势。该团队将使用标准尺度测量与对话伙伴的感知社会联系以及机器人的可接受性，以及询问有关体验细节的问题，以更深入地了解手臂有效的原因（如果是的话），并指导未来系统的设计。

This project will study how making common telepresence robots more expressive and interactive affects people's willingness to use them and their opinions of remote collaborators. Many telepresence robots take the form of a screen on a mobile platform, giving remote attendees a physical body that can increase feelings of presence and interaction compared to a normal videoconference. However, the limited non-verbal expressiveness of these platforms is a major barrier to their use. Thus, the research goal is to increase the embodiment and social interaction capabilities of these platforms by adding a hand and arm to support common non-verbal interactions such as pointing, gesturing, and touch. To do this, the researchers will first build a simple hand and arm to support these non-verbal interactions and add it to an existing telepresence robot. They will then develop software to run on the robot to execute the gestures and ensure the safety of people nearby, as well as a user interface that maps gestures by the remote user onto gestures the hand and arm are able to execute. They will then test the usability of the system and its effects on social presence through studies that include both one-on-one and small group icebreaking conversations. This work will lead toward more natural interfaces for telepresence robots and a better understanding of how people accept them and interact with them. This, in turn, should lead to social benefits by making remote interaction more effective, saving time, effort, and fuel costs around travel while supporting not just remote meetings but other remote services such as medical diagnosis and caregiving. The team will also use the research both for their own classes and for outreach at events designed to encourage children to explore science careers. The work sits at the intersection of telerobotics, haptics, and social psychology. Because gestures, pointing, handshakes, and other non-verbal communication are an important part of human interaction that current telepresence platforms do not support, the work focuses on the development of a lightweight arm that can implement those gestures without the complexity, fragility, and expense of arms that fully mimic human motion. To make this tradeoff, the research team will develop a 3D-printed, 5-fingered hand with 3 degrees of freedom and simple connections that allow the fingers to bend naturally enough to recreate the intended gestures. The control software will use a forward and inverse kinematics approach to model hand configurations and use an open-loop controller that works along with the human operator to implement the gestures; bump, force, and optical sensors will be used to address safety concerns. For the human remote operator, the team will develop interfaces that (a) add cameras to the robot to provide a fuller view of the remote environment needed for effective gestures, and (b) use motion-tracking hardware to detect the remote user's arm motion and translate it into the space of possible motions of the robot arm, focusing on the specific targeted social gestures. They will evaluate the system through a series of between-subjects user studies, having participants as either the remote or local user interacting with a version of the robot with or without the arm. Participants will interact with trained experimental confederates both to reduce variability and to ensure that the target non-verbal gestures are experienced in both the with- and without-arm conditions. The team will measure perceived social connectedness with conversation partners and acceptability of the robot using standard scales, as well as asking questions about the particulars of the experience both to gain deeper insight into the reasons why the arm is effective (if it is) and to guide the design of future systems.