Researchers at the Massachusetts Institute of Technology (MIT) have achieved a significant breakthrough with the creation of the mmNorm system, which enables robots to detect and create accurate 3D models of objects hidden from direct sight. This innovation opens doors for applications such as identifying defective goods in sealed packaging without needing to open them.

The technology is founded on millimeter-wave (mmWave) radio waves, the same type utilized in Wi-Fi and 5G. These waves possess the ability to penetrate various materials, including plastic containers and cardboard boxes, and reflect off objects concealed within. A key distinction of mmNorm from previous methods lies in its capacity to analyze not only the signal's position in space but also the surface orientation of the object at each point. By computing the surface normal, mmNorm can precisely reconstruct the curvature and overall three-dimensional form of a hidden object.

The system achieves an impressive 96% accuracy in reconstructing everyday objects with complex curved shapes, significantly surpassing the 78% achieved by prior methods. According to Fadel Adib, one of the study's authors, this was made possible by a "fundamentally new approach to signal processing." Such high efficiency presents broad application possibilities: from quality control in factories and goods sorting to assistance in nursing homes, and for robots that need to identify tools in closed drawers, even distinguishing their handles for precise grasping. It is important to note that mmNorm performs well with objects made of wood, metal, plastic, rubber, and glass; however, it is not yet suitable for scanning objects hidden behind metal barriers or very thick walls.

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