Image by Creative Machine Labs, from EurekAlert
Columbia Builds Robots That Grow by Consuming Other Robots
- Written by Kiara Fabbri Former Tech News Writer
- Fact-Checked by Sarah Frazier Former Content Manager
Columbia University scientists have developed an advanced robotic system mimic living organisms. These robots have the ability to grow, repair, and adapt by using materials from the environment, or even other robots.
In a rush? Here are the quick facts:
- The process is called “Robot Metabolism.”
- Truss Links are modular bars enabling self-assembly and adaptation.
- Robots improved speed by 66.5% by adding new parts.
The innovation, known as Robot Metabolism, allows machines to physically to transform, thus creating possibilities for autonomous robotics.
“True autonomy means robots must not only think for themselves but also physically sustain themselves,” Philippe Martin Wyder, lead author of the study, said in the study announcement . “Just as biological life absorbs and integrates resources, these robots grow, adapt, and repair using materials from their environment or from other robots.”
At the core of the project is the Truss Link, a magnetic, bar-shaped module inspired by children’s Geomag toys. The simple sticks connect freely to create expanding structural systems that generate increasingly complex robots with functional capabilities.
Researchers demonstrated in their study that these Truss Links can form two-dimensional shapes and then fold into 3D machines. In one test, a tetrahedron robot added a limb to improve its downhill speed by over 66.5%.
Hod Lipson, co-author and director of the Creative Machines Lab at Columbia, explained, “Robot minds have moved forward by leaps and bounds in the past decade through machine learning, but robot bodies are still monolithic, unadaptive, and unrecyclable.“
Lipson then added, “Biological bodies, in contrast, are all about adaptation – lifeforms, can grow, heal, and adapt. In large part, this ability stems from the modular nature of biology that can use and reuse modules (amino acids) from other lifeforms. Ultimately, we’ll have to get robots to do the same – to learn to use and reuse parts from other robots. You can think of this nascent field as a form of ‘machine metabolism.’”
The research suggests that future robots could one day build or upgrade themselves on the fly, which would make them suitable for disaster response, space exploration, as well as remote investigation areas with restricted human accessibility.
“Robot Metabolism provides a digital interface to the physical world and allows AI to not only advance cognitively, but physically,” Wyder added. “Ultimately, it opens up the potential for a world where AI can build physical structures or robots just as it today writes or rearranges the words in your email,” Wyder noted.
While the concept raises sci-fi concerns about self-replicating machines, Lipson is clear: “We can’t rely on humans to maintain these machines. Robots must ultimately learn to take care of themselves.”
But the field is still in its early stages and has major limitations. The current robot structures are simple, due to design and hardware constraints. Each Truss Link costs over $200 and lacks advanced sensors.
The robots are operator-controlled and cannot yet make decisions on their own. “This is a direct result of the still nascent stage of the field,” the team noted. Challenges like weight, expansion limits, and communication between modules remain.
The research establishes vital advancements toward the development of resilient, self-sustaining robots, which the team describes as robot ecology.
Image by Brett Jordan, from Unsplash
Your Body Distorts Wi-Fi, That’s How Hackers Can Track You
- Written by Kiara Fabbri Former Tech News Writer
- Fact-Checked by Sarah Frazier Former Content Manager
Scientists at La Sapienza University in Rome developed a new system to detect people through Wi-Fi signals, without the need of cameras or phones.
In a rush? Here are the quick facts:
- It works even if the person carries no device or phone.
- Transformer-based models achieved up to 95.5% accuracy on NTU-Fi dataset.
- Wi-Fi-based tracking works through walls and in poor lighting conditions.
The system, named WhoFi, captures how a person’s body distorts Wi-Fi signals and turns that pattern into a unique biometric signature.
“The core insight is that as a Wi-Fi signal propagates through an environment, its waveform is altered by the presence and physical characteristics of objects and people along its path,” the authors explain to The Register .
The recorded changes appear in Channel State Information (CSI), which contains detailed biometric information, like how bones and organs affect signal paths.
In their research paper , the team explains how they trained a deep neural network to recognize these body-specific distortions. Using a Transformer-based encoder, the WhoFi system achieved up to 95.5% accuracy on a public dataset.
The Register notes that that’s a major improvement over previous methods like 2020’s EyeFi, which reached around 75% accuracy.
In contrast to traditional video-based surveillance, which can struggle in poor lighting or with obstructed views, Wi-Fi-based identification is unaffected by darkness and can even see through walls. Researchers say this makes it more reliable and potentially more privacy-preserving, since it doesn’t capture images.
The authors clarify that ‘re-identification’ technology does not expose personal names, but it can confirm that one person has been spotted at different locations. The system enables tracking of individuals between Wi-Fi areas through their unique signal patterns, without carrying a device.
“The encouraging results achieved confirm the viability of Wi-Fi signals as a robust and privacy-preserving biometric modality, and position this study as a meaningful step forward in the development of signal-based Re-ID systems,” the authors say to The Register..
This new technology will likely raise new privacy and surveillance ethics concerns.
