SocioGesture：面向人机交互的实时自适应社交手势感知
SocioGesture: Real-Time and Adaptive Social Gesture Perception for Human-Robot Interaction
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中文总结 AI 辅助
提出SocioGesture系统，采用置信感知骨架与双流模型，提升社交手势识别的鲁棒性与实时性，可扩展词汇且适配机器人部署。
中文摘要 AI 辅助
与人类交互的机器人不仅需要识别显式指令，还需识别邀请、拒绝、不可用状态等社交线索。在实际部署中，这些线索必须从带噪声的机载感知数据中推断，且需应对部分遮挡、视角变化及严格的延迟约束。本文提出SocioGesture，一种面向人机交互（HRI）的实时自适应社交手势感知系统。SocioGesture采用紧凑的置信感知体-手骨架表示与轻量双流模型，融合肢体运动与手部关节信息，实现低延迟机载识别。为提升部署鲁棒性，我们采用感知遮挡的骨架损坏数据训练模型，使其能应对手部缺失、手臂遮挡、时间不稳定关键点等情况，且不增加推理成本。在混合室内外HRI场景收集的社交手势数据集上，SocioGesture在留取受试者识别任务中表现优异，显著提升了结构化关节遮挡下的鲁棒性，且可在机器人搭载的边缘设备上实时运行。部署期间，不确定的交互片段会被保存用于离线标注与适配，使SocioGesture能扩展手势词汇，同时保持原类别性能。这些结果为交互式机器人实现鲁棒、高效、自适应的社交感知提供了可行路径。
英文摘要
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.
发表机构
- OpenMind
机构由 AI 辅助整理，请以论文原文为准。
