# WaveMan: mmWave-Based Room-Scale Human Interaction Perception for Humanoid Robots

arXiv: https://arxiv.org/abs/2601.07454 · HTML: https://mlsanity.com/paper/2601.07454/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jan 2026.

## Built on by (most cited first, 3 of 3)

- [Wave2Body: Rethinking mmWave Human Pose Estimation as Radar-to-Body Token Translation](https://mlsanity.com/paper/2607.18875/) (2026)
- [mmHRI: Towards Privacy-Preserving Human-Robot Interaction with Millimeter-Wave Radar](https://mlsanity.com/paper/2609.34220/) (2026)
- [Learning to Assess Heartbeat Observability for mmWave Heart-Rate Sensing](https://mlsanity.com/paper/2610.03570/) (2026)

## Abstract (arXiv)

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.