Google DeepMind announced Gemini Robotics 2.5, a vision-language-action model designed to help robots learn a wider range of tasks more quickly. Evaluated across 200 tasks, the model improved success rates by 30% to 100% compared with previous versions and completed tasks 20% to 30% faster. In a trial on a Mercedes-Benz production line, Apptronik’s Apollo 2 humanoid robot sorted and packed automotive parts with a 96% success rate. This version also strengthens operator control and resistance to adversarial manipulation, alongside a new benchmark for evaluating robotic defenses against such attacks.
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