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Genesis AI has introduced GENE-26.5, a robotics foundation model designed to support human-level physical manipulation in robots. The company also presented a dexterous robotic hand and data collection system intended to address one of robotics’ main limits, the lack of scalable, high-quality training data.

For eeNews Europe readers, the announcement is relevant because it connects AI foundation models with real-world robotic manipulation, including electronics-related tasks such as wire harnessing and lab automation. It also points to how data collection hardware could influence the pace of robotics development in industrial environments.

Robotics foundation model for manipulation

GENE-26.5 is described as a robotic brain built to absorb data from many environments and apply it to long-horizon physical tasks. Genesis AI demonstrated the system in a video showing activities including cooking, preparing a smoothie, pipetting in lab work, wire harnessing, solving a Rubik’s Cube, multi-object grasping and piano playing.

“The brain and hand are the two most valuable and complex pieces of robotics, and today we are presenting the industry’s most advanced versions of both,” said Zhou Xian, Co-Founder and CEO of Genesis AI. “For the first time ever, we’re enabling robots to do what only human hands could, and do it reliably, at scale.”

Data engine addresses robotics bottleneck

A central part of the GENE-26.5 approach is Genesis AI’s human-scale dexterous robotic hand, paired with a glove using tactile-sensing electronic skin. The company says this creates a 1:1:1 mapping between the glove, the human hand and the robotic hand, allowing human demonstrations to transfer more directly into robotic skills.

Genesis AI says its glove is 100 times cheaper than typical options in hardware cost and, in internal testing, achieved up to five times greater data collection efficiency than traditional teleoperation methods. The company plans to deploy the glove with partners in real-world work environments to build a human skill library.

Simulation supports model training

Genesis AI is also using simulation to narrow the sim-to-real gap, with physics and rendering intended to make virtual training more representative of real-world conditions. The company says this could help teams train and evaluate models faster than physical testing alone.

“At Genesis, we believe winning in robotics requires excellence at every level,” said Theophile Gervet, Co-Founder and President of Genesis AI. “That’s why we’re obsessed with innovating across the full-stack, from AI to hardware. By controlling every layer, we can build a cohesive system and solve the problem holistically. Our approach gives us a huge competitive advantage by harnessing unprecedented amounts of data as that ultimately defines what foundation models can achieve.”

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