ReMotion: Supporting Remote Collaboration in Open Space with Automatic Robotic Embodiment

要旨

Design activities, such as brainstorming or critique, often take place in open spaces combining whiteboards and tables to present artefacts. In co-located settings, peripheral awareness enables participants to understand each other’s locus of attention with ease. However, these spatial cues are mostly lost while using videoconferencing tools. Telepresence robots could bring back a sense of presence, but controlling them is distracting. To address this problem, we present ReMotion, a fully automatic robotic proxy designed to explore a new way of supporting non-collocated open-space design activities. ReMotion combines a commodity body tracker (Kinect) to capture a user’s location and orientation over a wide area with a minimally invasive wearable system (NeckFace) to capture facial expressions. Due to its omnidirectional platform, ReMotion embodiment can render a wide range of body movements. A formative evaluation indicated that our system enhances the sharing of attention and the sense of co-presence enabling seamless movement-in-space during a design review task.

著者
Mose Sakashita
Cornell University, Ithaca, New York, United States
Ruidong Zhang
Cornell University, Ithaca, New York, United States
Xiaoyi Li
Cornell University , Ithaca, New York, United States
Hyunju Kim
Cornell University, Ithaca, New York, United States
Michael Russo
Cornell University, Ithaca, New York, United States
Cheng Zhang
Cornell University, ITHACA, New York, United States
Malte F. Jung
Cornell University, Ithaca, New York, United States
Francois Guimbretiere
Cornell University, Ithaca, New York, United States
論文URL

https://doi.org/10.1145/3544548.3580699

動画

会議: CHI 2023

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2023.acm.org/)

セッション: Human-AI collaboration

Hall B
6 件の発表
2023-04-25 20:10:00
2023-04-25 21:35:00