Google DeepMind’s Gemini Robotics ER 2 could reshape the automation economy, and crypto should pay attention

Google DeepMind released Gemini Robotics ER 2, an AI robotics model that enables multi-robot collaboration and real-time planning through temporal intelligence, achieving 91.3% accuracy for moment finding with sub-second precision. The model supports third-party hardware including Boston Dynamics' Spot and Apptronik's Apollo, positioning Google as an operating system layer for robotics with implications for the automation economy.

Via 9to5google.com Google DeepMind’s Gemini Robotics ER 2 could reshape the automation economy, and crypto should pay attention The latest AI robotics model from Google enables multi-robot collaboration and real-time planning, with implications that stretch well beyond factory floors. Google DeepMind just dropped Gemini Robotics ER 2, and it’s the kind of update that makes you rethink what robots are actually capable of. The new model doesn’t just make individual robots smarter. It makes groups of completely different robots work together like they share the same brain. Boston Dynamics’ Spot and Apptronik’s Apollo, two robots that look nothing alike and do very different things, can now coordinate through a shared semantic understanding powered by Google’s AI. What Gemini Robotics ER 2 actually does The core innovation here is what DeepMind calls “temporal intelligence.” In English: the robot can watch what’s happening through a continuous video feed, understand where it is in a multi-step task, and plan its next move accordingly. The numbers tell an interesting story. The model hits 57.4% accuracy for progress classification and 91.3% for moment finding, with a mean error of just 0.96 seconds. Progress classification, the ability to understand how far along a task is, still has room to grow. But moment finding, pinpointing exactly when a specific event occurs in a video stream, is remarkably precise at sub-second accuracy. The integration with Google’s Gemini Live API adds another layer. It enables low-latency, bidirectional streaming between the AI model and the robot, which means the system can adjust in real time without the awkward pauses that plagued earlier robotic controllers. Safety got a meaningful upgrade too. Improved instruction-following benchmarks and better proximity detection mean these robots are less likely to cause harm to human co-workers. The building blocks trace back to 2025 ER 2 didn’t appear out of nowhere. It builds on a foundation Google started laying in March 2025 with the initial Gemini robotics models, followed by a September update that same year. Each iteration has progressively expanded what robots can do with generative AI, moving from basic task execution to the kind of agentic behavior that lets machines make contextual decisions on the fly. Google isn’t operating in a vacuum either. The decision to support third-party hardware like Boston Dynamics and Apptronik robots suggests Google wants to be the operating system layer for robotics, not just a hardware vendor. Disclosure: This article was edited by Editorial Team. For more information on how we create and review content, see our Editorial Policy https://cryptobriefing.com/editorial-policy/ .