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SocioGesture: Real-Time and Adaptive Social Gesture Perception for Human-Robot Interaction

Summary

Robots in human-robot interaction need to understand social cues like invitations, refusals, and unavailability, not just explicit commands. SocioGesture is a real-time adaptive system that uses confidence-aware body-hand skeletons and a lightweight dual-stream model for low-latency onboard recognition. With occlusion-aware skeleton corruption during training, it remains robust under missing hands, occluded arms, and unstable keypoints. Tested on indoor-outdoor HRI datasets, it achieves strong generalization to unseen subjects, improves robustness under structured joint occlusion, and runs in real time on edge devices. Uncertain segments can be saved for offline labeling to expand the gesture vocabulary without hurting existing classes.

SourcearXiv RoboticsAuthor: Wenjin Fu, Li-Fan Wu, Jerin Peter, Chip Huyen, Boyuan Chen, Jan Liphardt
SocioGesture: Real-Time and Adaptive Social Gesture Perception for Human-Robot Interaction
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[Submitted on 3 Sep 2026]

Title:SocioGesture: Real-Time and Adaptive Social Gesture Perception for Human-Robot Interaction

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Abstract:Robots interacting with people must recognize not only explicit commands, but also social cues such as invitations, refusals, and unavailability. In real deployments, these cues must be inferred from noisy onboard perception under partial occlusion, changing viewpoints, and strict latency constraints. We present SocioGesture, a real-time adaptive social gesture perception system for human-robot interaction (HRI). SocioGesture uses a compact confidence-aware body-hand skeleton representation and a lightweight dual-stream model that fuses body motion with hand articulation for low-latency onboard recognition. To improve deployment robustness, we train the model with occlusion-aware skeleton corruption, exposing it to missing hands, occluded arms, and temporally unstable keypoints without increasing the inference cost. On a social gesture dataset collected in mixed indoor-outdoor HRI scenarios, SocioGesture achieves strong held-out-subject recognition, substantially improves robustness under structured joint occlusion, and runs in real time on a robot-mounted edge device. During deployment, uncertain interaction segments are saved for offline labeling and adaptation, enabling SocioGesture to expand its gesture vocabulary while preserving performance in the original classes. These results demonstrate a practical path toward robust, efficient, and adaptive social perception for interactive robots.

Comments: 15 pages, 3 figures. Project page: this https URL

Subjects:

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

Cite as: arXiv:2609.04545 [cs.RO]

(or arXiv:2609.04545v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2609.04545

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Wenjin Fu [view email] [v1] Thu, 3 Sep 2026 23:03:43 UTC (4,911 KB)

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Key points and analysis

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Key points

  • Uses a confidence-aware body-hand skeleton representation and a lightweight dual-stream model for low-latency robot onboard recognition.
  • Applies occlusion-aware skeleton corruption during training to boost robustness under missing or unstable keypoints without added inference cost.
  • Achieves strong held-out-subject performance on indoor-outdoor HRI data and robust gains under structured joint occlusion.
  • Runs real time on a robot-mounted edge device and supports adaptive gesture vocabulary expansion during deployment.

Highlights and analysis are generated automatically and may contain errors. Check the original source.

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