Reliable humanoid-robot interaction (HRI) in household environments is constrained by two fundamental requirements, namely robustness to unconstrained user positions and preservation of user privacy. Millimeter-wave (mmWave) sensing inherently supports privacy-preserving interaction, making it a promising modality for room-scale HRI. However, existing mmWave-based interaction-sensing systems exhibit poor spatial generalization at unseen distances or viewpoints. To address this challenge, we introduce WaveMan, a spatially adaptive room-scale perception system that restores reliable human interaction sensing across arbitrary user positions. WaveMan integrates viewpoint alignment and spectrogram enhancement for spatial consistency, with dual-channel attention for robust feature extraction. Experiments across five participants show that, under fixed-position evaluation, WaveMan achieves the same cross-position accuracy as the baseline with five times fewer training positions. In random free-position testing, accuracy increases from 33.00% to 94.33%, enabled by the proposed method. These results demonstrate the feasibility of reliable, privacy-preserving interaction for household humanoid robots across unconstrained user positions.

翻译：在家庭环境中实现可靠的人形机器人交互面临两个基本约束：对用户无约束位置的鲁棒性以及用户隐私保护。毫米波感知技术本身支持隐私保护的交互，使其成为房间尺度人机交互的一种有前景的模态。然而，现有的基于毫米波的交互感知系统在未见的距离或视角下表现出较差的空间泛化能力。为应对这一挑战，我们提出了WaveMan——一种空间自适应的房间尺度感知系统，可在任意用户位置恢复可靠的人机交互感知。WaveMan集成了视角对齐与频谱图增强以实现空间一致性，并采用双通道注意力机制进行鲁棒特征提取。在五位参与者上进行的实验表明，在固定位置评估中，WaveMan仅需基线方法五分之一数量的训练位置即可达到相同的跨位置准确率。在随机自由位置测试中，准确率从33.00%提升至94.33%，这得益于所提出的方法。这些结果证明了在无约束用户位置下，为家庭人形机器人实现可靠且保护隐私的交互具有可行性。