Genesis AI unveils robotic brain with dexterous hand and data engine enabling scalable robot training systems new era.
The company also introduced a scalable training system aimed at solving the long-standing data shortage in robotics foundation models. The platform combines a proprietary human-scale dexterous robotic hand, which allows direct transfer of human skills to robots, with a new high-capacity data engine.
Genesis says the system can generate unlimited training data, enabling the development of more capable and productive general-purpose robots for real-world applications. The system is built to help robots perform complex, long-duration tasks with advanced dexterity and human-like precision. According to the company, GENE-26.5 is designed to enable robots to quickly adapt to unfamiliar environments and new tasks without extensive retraining.
To demonstrate the model’s capabilities, Genesis released a video showing robots completing some of the most advanced manipulation tasks achieved so far. The demonstrations highlight smooth hand coordination, precise movements, and human-like control across a variety of activities. In one sequence, the robot prepares a 20-step meal that includes chopping tomatoes, cracking eggs with one hand, and coordinating both hands during cooking.
Another demonstration shows the robot making a smoothie by handling ingredients, pouring liquids, blending, and serving the drink mid-air using two-hand coordination. The system also performs delicate laboratory tasks such as pipetting and liquid transfer, wire harnessing for electronics assembly, solving a Rubik’s Cube through continuous in-air manipulation, sorting multiple objects with one hand, and playing a fast-paced piano composition, according to aand data collection system designed to reduce the “embodiment gap” between humans and robots.
The company says differences between human and robotic hand structures have long limited robots’ ability to learn effectively from human-generated data.hand closely matches the form and movement of a human hand and works alongside a wearable glove fitted with tactile-sensing electronic skin. The system creates a direct 1:1:1 mapping between the human hand, the glove, and the robotic hand, allowing human actions to be accurately transferred into robotic training data.
According to Genesis, the glove system is 100 times cheaper than conventional hardware solutions. Internal testing also showed up to five times greater data collection efficiency and higher-quality results compared to traditional teleoperation systems. The company plans to deploy the gloves in real-world workplaces through industry partnerships. Workers can wear the gloves while performing routine tasks, allowing robots to learn directly from everyday human activities.
Genesis AI says this approach will help create a large-scale library of human skills for robotics training. Beyond glove-based data, the company’s data engine also uses egocentric videos recorded from wearable cameras and publicly available human-centered internet videos. Genesis AI says combining these data sources improves robotic learning efficiency and enables robots to perform more advanced physical tasks. Jijo is an automotive and business journalist based in India.
Armed with a BA in History from St. Stephen's College, Delhi University, and a PG diploma in Journalism from the Indian Institute of Mass Communication, Delhi, he has worked for news agencies, national newspapers, and automotive magazines. In his spare time, he likes to go off-roading, engage in political discourse, travel, and teach languages. AI and Robotics
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