Millimeter-wave (mmWave) radar perception is bottlenecked by training data: every room reflects mmWave differently, so models do not transfer and labeling radar in each new space is impractical. We demonstrate a material-aware RF digital twin for object recognition with no real radar labels. From a 3D scan the twin inf...
Emily Bejerano, Federico Tondolo, Ayaan Qayyum et al.· Proceedings of the ACM Works...· 0 citations
This work introduces \the authors', which compresses frame-to-frame changes into latent actions and predicts next-frame features during end-to-end video--language alignment and achieves competitive recognition with faster inference and smaller INT4 accuracy drops than V-JEPA2/2.1.
Jiajun Cheng, Sainan Liu, Subarna Tripathi et al.· 0 citations
This work introduces the first comprehensive benchmark for evaluating pretrained Vision-Language Models on zero-shot multi-arm fruit harvesting planning and shows that frontier VLMs can generate effective multi-arm harvesting plans zero-shot, but a practical deployment remains limited by accurate 3D waypoint generation...
Vrishan Inukollu, Adyan Zaman, Anvi Kudaraya et al.· 0 citations
The results show that zero-shot LLM agents can achieve comparable management outcomes to RL agents under the same weather pattern and adapt more effectively than RL when evaluated under a shifted environment, highlighting a promising path toward self-adaptive physical AI agents.
OrthoFL is proposed, an orthogonal calibration framework that decouples global and local learning progress to reduce interference and consistently outperforms state-of-the-art asynchronous baselines under various delay patterns and heterogeneity scenarios.
Jiayun Zhang, Shuheng Li, Haiyu Huang et al.· Proceedings of the 32nd ACM...· 0 citations
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