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This paper presents a research testbed, a furnished home instrumented with 48 synchronized cameras and a distributed perception pipeline, for studying how robots respond to people. Its results focus on how camera coverage, latency, and learned pose, hand, gaze, and forecasting models affect task performance in safety yielding and intent recognition. For AI readers, the useful takeaway is the empirical evidence on which perception components matter most for real-world human-robot assistance, not the hardware itself.
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OmniRobotHome: A Multi-Camera Home Platform for Real-Time Human-Robot Interaction

arXiv2 min read

arXiv:2604.28197v3 Announce Type: replace Abstract: Robots that assist people must respond promptly and appropriately to human activity, which requires low-latency processing, accurate perception, and anticipation of human motion. These requirements are especially difficult to meet in living environments, where perception must cover wide areas, remain robust to occlusion, and capture natural behavior without wearable devices. We present OmniRobotHome, a testbed that addresses these challenges in a fully furnished home that supports natural everyday activity. Its 48 hardware-synchronized cameras provide room-wide coverage, enabling accurate markerless perception under occlusion. A distributed perception pipeline processes these streams in parallel and delivers persistent human and object states to robot controllers with low latency. We demonstrate OmniRobotHome through two human-robot interaction studies: proactive yielding for safety and nonverbal intent recognition for assistance. Through these studies, we analyze how camera coverage, state latency, multi-view pose, hand, and gaze estimation, and behavior-memory-based forecasting affect overall task performance. Our results highlight the value of OmniRobotHome as a testbed for studying human-robot assistance in real living environments.

Abstract:Robots that assist people must respond promptly and appropriately to human activity, which requires low-latency processing, accurate perception, and anticipation of human motion. These requirements are especially difficult to meet in living environments, where perception must cover wide areas, remain robust to occlusion, and capture natural behavior without wearable devices. We present OmniRobotHome, a testbed that addresses these challenges in a fully furnished home that supports natural everyday activity. Its 48 hardware-synchronized cameras provide room-wide coverage, enabling accurate markerless perception under occlusion. A distributed perception pipeline processes these streams in parallel and delivers persistent human and object states to robot controllers with low latency. We demonstrate OmniRobotHome through two human-robot interaction studies: proactive yielding for safety and nonverbal intent recognition for assistance. Through these studies, we analyze how camera coverage, state latency, multi-view pose, hand, and gaze estimation, and behavior-memory-based forecasting affect overall task performance. Our results highlight the value of OmniRobotHome as a testbed for studying human-robot assistance in real living environments.

Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)

arXiv:2604.28197 [cs.RO]

(or arXiv:2604.28197v3 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2604.28197 Focus to learn more arXiv-issued DOI via DataCite

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Full text under the publisher's open licence · 260 words

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