A groundbreaking new imaging technique has been developed by researchers at the Massachusetts Institute of Technology (MIT) that could revolutionize quality control processes in warehouses. This innovative system allows robots to use reflected Wi-Fi signals to identify the shape of hidden 3D objects, making it particularly useful in warehouse and factory settings.
The system, known as mmNorm, leverages millimeter wave (mmWave) signals – the same signals used in Wi-Fi technology – to create precise 3D reconstructions of objects that are obstructed from view. These waves have the ability to penetrate through common obstacles such as plastic containers or interior walls, and bounce off hidden objects. By collecting and analyzing these reflections, the system’s algorithm can accurately estimate the shape of the object’s surface.
In testing, the new approach achieved an impressive 96% reconstruction accuracy for a variety of everyday objects with complex shapes, surpassing the 78% accuracy achieved by current state-of-the-art methods. Remarkably, mmNorm achieves this high level of accuracy without requiring additional bandwidth, making it an efficient and versatile solution for various applications.
One potential application of mmNorm is in enabling robots to differentiate between tools hidden in a drawer and identify their handles, allowing them to handle objects more effectively without causing damage. This capability could be beneficial in a wide range of settings, from factories to assisted living facilities.
“We’ve been interested in this problem for quite a while, but we’ve been hitting a wall because past methods, while they were mathematically elegant, weren’t getting us where we needed to go. We needed to come up with a very different way of using these signals than what has been used for more than half a century to unlock new types of applications,” explains Fadel Adib, associate professor in the Department of Electrical Engineering and Computer Science at MIT and senior author of the paper on mmNorm.
Adib collaborated with research assistants Laura Dodds, Tara Boroushaki, and former postdoc Kaichen Zhou to develop this groundbreaking imaging technique. Their work signifies a significant advancement in the field of robotics and imaging technology, with the potential to transform various industries and improve efficiency in automated processes. A groundbreaking research study was recently unveiled at the prestigious Annual International Conference on Mobile Systems, Applications and Services (ACM MobiSys 2025), which took place in Anaheim from June 23 to 27. The study, conducted by a team of researchers from MIT, focused on revolutionizing traditional radar techniques by introducing a new method called mmNorm.
Traditional radar methods typically involve sending mmWave signals and capturing reflections from the environment in order to detect hidden or distant objects. However, these techniques often result in coarse image resolutions, making it challenging to identify small objects that may be crucial for certain tasks, such as kitchen gadgets that a robot needs to recognize.
Upon delving deeper into this issue, the MIT researchers discovered that existing back projection techniques overlook a critical property known as specularity. When mmWaves are transmitted by a radar system, most surfaces act as mirrors, generating specular reflections. The direction in which a surface is pointed plays a crucial role in determining whether the reflection will be received by the radar or not.
Building on this insight, the researchers developed mmNorm, a cutting-edge technology designed to estimate surface normals – the directions of surfaces at specific points in space. By utilizing these estimations to reconstruct the curvature of the surface at each point, mmNorm employs a unique mathematical formulation to reconstruct 3D objects.
To test the efficacy of mmNorm, the researchers constructed a prototype by attaching a radar to a robotic arm, which continuously takes measurements as it moves around a concealed item. By comparing the strength of signals received at different locations, the system can accurately estimate the curvature of the object’s surface.
One of the most impressive features of mmNorm is its ability to distinguish between multiple objects with complex shapes, such as mugs with handles and curves. In fact, the technology was able to generate reconstructions with significantly less error than existing techniques, while also providing more accurate estimations of an object’s position.
Moreover, mmNorm showcased excellent performance across a variety of materials, including wood, metal, plastic, rubber, and glass. However, it may struggle with objects concealed behind thick metal or walls.
The researchers believe that the advancements made with mmNorm have vast implications for various applications. For example, robots equipped with this technology can easily differentiate between tools in a box, accurately determine the shape and location of a hammer’s handle, and plan accordingly to use it for a specific task.
In conclusion, the introduction of mmNorm represents a major leap forward in radar technology, offering high-resolution 3D reconstructions that can be leveraged for a wide range of innovative applications. The successful implementation of this groundbreaking technology paves the way for exciting advancements in the field of robotics and object detection.
