arXiv:2605.30818v1 Announce Type: cross Abstract: Non-contact material identification enables adaptive interaction for embodied intelligence yet faces challenges from geometry-induced variations (e.g., orientation, shape, distance) and single-modality ambiguities. In this paper, we present GaMi, a multimodal material identification system integrating mmWave and acoustic sensing to robustly operate under unconstrained geometric conditions. By leveraging the insight of shared geometric consistency between co-located bimodal sensors, GaMi employs an intra-sample cross-modal subtractive disentangl
The development of robust and geometry-agnostic material identification systems is crucial as embodied intelligence, like humanoid robots and AI agents, moves into real-world applications requiring nuanced interaction with diverse materials.
This technology enables adaptive interaction for embodied AI, foundational for their effective operation in unconstrained environments where precise material understanding is necessary for tasks ranging from manipulation to manufacturing.
Embodied AI systems can now identify materials more reliably regardless of their shape, orientation, or distance, significantly improving their functional intelligence and adaptability in complex physical spaces.
- · Robotics manufacturers
- · AI hardware developers
- · Logistics and manufacturing sectors
- · Advanced sensing technology companies
- · Systems relying solely on single-modality material identification
Embodied AI becomes more versatile and capable of handling diverse objects and environments without extensive pre-programming.
This capability accelerates the deployment of AI-driven automation in unstructured settings, expanding the scope of tasks suitable for integration.
Improved material interaction could enable novel manufacturing processes, personalized product creation, and more efficient resource management.
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Read at arXiv cs.AI