Dozens of new robot arms have been installed at General Motors’ flagship electric vehicle factory in Detroit—even as 1,300 workers remain out of work following what was supposed to be a temporary layoff. The latest automation push has spurred union pushback over a potentially existential issue for automakers and their workers.

General Motors installed approximately 50 robot arms at GM’s Factory Zero plant in Detroit, Michigan, according to reporting by Crain’s Detroit Business. Made by the Japanese robotics company FANUC, the robots are designed to help attach various components to vehicles during the assembly line process. But leaders at United Auto Workers (UAW), the primary US union for autoworkers, reacted with anger to the new robotic presence, given how GM has not yet called back any of the workers affected by supposedly temporary layoffs in March.

More than 1,000 union members are still “laid off indefinitely,” James Cotton, president of UAW Local 22, told The Detroit News. He said that the company could bring some of those members back to work instead of installing the 50 robots.

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What happens when you give AI coding agents a lab full of robotic arms, some compute resources, and a “generous token budget” for teaching the robots various tasks? The agents can apparently figure out a training regimen that teaches the robots to successfully cut zip ties and even insert GPUs into thin sockets on motherboards.

That glimpse into how AI can act in a fully autonomous way to automate robot training was made possible by a new agent harness framework—software that wraps around AI models to enable their use of various tools while also providing capabilities such as memory, context, constraint, and feedback loops. That agentic harness, called ENPIRE, was developed by robotics researchers at the Nvidia GEAR (Generalist Embodied Agent Research) lab alongside collaborators from Carnegie Mellon University in Pittsburgh and the University of California, Berkeley.

“A part of our NVIDIA GEAR lab now self-improves tirelessly overnight,” wrote Jim Fan, director of AI at NVIDIA, in a LinkedIn post. “We just read the reports in the morning.”

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Fully autonomous drones killed Russian soldiers during a battlefield test two years ago, according to a Ukrainian drone manufacturer. If true, the incident would represent another milestone in a war that has spurred unprecedented developments in military drones, robots, and AI-guided weaponry.

The one-time test was revealed by Alexander Kokhanovskyy, CEO of the Ukrainian drone maker Aero Center, during an interview with New Scientist at a press event hosted by the Ukrainian embassy in London. Kokhanovskyy described the test—which did not involve his current company Aero Center—using quadcopter drones that were preprogrammed to fly to a front-line area before activating an AI-powered “Terminator mode” that would seek out and attack any target in the given area.

There was apparently no video feed or anything else to show what the “Terminator” drones targeted and attacked. But Kokhanovskyy told New Scientist that human-piloted drones sent to check out the aftermath found “a couple” of dead Russian soldiers, which led to the conclusion that the fully autonomous drones had killed them.

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A decade after the global craze for Pokémon Go peaked, an AI company has been using billions of real-world images captured by millions of players to develop navigation technologies for delivery robots and possibly military drones. That represents an intriguing but potentially discomfiting legacy for an augmented reality mobile game that has incentivized gamers to capture short smartphone videos of physical neighborhoods and landmarks.

The AI company, Niantic Spatial, was spun out of Pokémon Go game developer Niantic in May 2025, after Niantic separately sold its licensed games such as Pokémon Go to the Saudi-backed video game publisher Scopely. But before that deal, Niantic publicly announced plans to use scans from millions of Pokémon Go players along with data captured by users of the company’s Scaniverse app to train and develop a “large geospatial model”—a 3D model of the physical world trained on the geolocated images provided by app users scanning real-world locations.

“Ground scans were one component to help train Niantic Spatial's real-world foundation models —AI systems that learn to recognize and interpret physical spaces,” a Niantic Spatial spokesperson told Ars. “The models are the product of that training, not a copy of or a means of accessing the underlying scans, which were of public points of interest such as statues and fountains.”

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It may appear that humanoid robots capable of handling any task have almost arrived—especially when tech companies showcase them performing acrobatic feats or handling household chores. But there is still a significant gap between these robot demonstrations and proving that the same robots can reliably and repeatedly manage such tasks in the real world.

The latest wave of robot videos can be particularly tricky, given the human tendency to anthropomorphize objects with a humanoid figure. A robot arm doing a dance move may simply seem “cool,” but a humanoid robot doing the same dance move can trigger more misleading assumptions, said Jonathan Hurst, cofounder of Agility Robotics and a robotics researcher at Oregon State University.

“People automatically extrapolate and assume that the robot that looks like a person can do all the things that a person who can dance could do—which is not true,” Hurst told Ars. “But a lot of the startup companies do kind of prey on that for being able to raise a lot of money.”

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