Google DeepMind launches Gemini Robotics 2

Google DeepMind's central technical claim is architectural, not just cosmetic. Prior systems, including the company's own Gemini Robotics 1.5, stitched together separate controllers for locomotion and manipulation at handoff points ^[1]. Gemini Robotics 2 replaces that seam with a single learned policy that simultaneously coordinates legs, torso, both arms, and a 22-degree-of-freedom hand ^[1]. The lineup splits into three models with distinct jobs: the vision-language-action model converts vision and language input directly into motor control, Gemini Robotics ER 2 handles communication, world understanding, and multi-step planning, and Gemini Robotics On-Device 2 is optimized to run locally on the robot itself ^[2]. In practice this shows up as one AI layer running on physically different hardware - the same benchmark suite reports results for the Apollo 2 humanoid, fitted with Inspire hands, and separately for the two-armed Franka Duo tabletop platform ^[3], which is the substance behind DeepMind's 'one brain for any robot' positioning rather than a bespoke model per machine.

The demo reel of knot-tying and lightbulb removal obscures a far more uneven benchmark picture. On multi-finger dexterity tasks, DeepMind's own reported numbers range from 92% success unscrewing a lightbulb down to 44% tying a trash bag, 40% sealing a ziplock bag, and just 32% on a dustpan task ^[3]. Whole-body manipulation success also swings by context - 76.3% picking objects from a shelf but only 45.7% from the floor on the Apollo 2 platform ^[3]. That spread matters because, per outside reporting, the showcased tasks were not evidence of open-ended generalization: the robots shown are not general-purpose machines, since the model was trained specifically for each task in the video using human teleoperation, video examples, and simulation ^[4]. The gap between dexterity success rates clustered anywhere from roughly a third to over ninety percent and a 'physical AGI' narrative is exactly where independent observers are pushing back.

Despite the breadth of the announcement, most of the stack is not available to try. Gemini Robotics ER 2, the reasoning and planning model, is currently available through Google AI Studio and in private preview on the Gemini Enterprise Agent Platform ^[5]. The vision-language-action model that actually drives motor control, and the On-Device model built for offline hardware adaptation, remain limited to selected early-access partners ^[5]. That means the part of the system the public can query today is the 'brain' that plans and reasons, not the part that moves a robot's fingers - a distinction easy to lose in coverage that treats the launch as a single unified product.

Google DeepMind frames the launch in explicitly maximalist terms. Head of robotics Carolina Parada has called it a step toward 'physical AGI' and said the safety question becomes even more pressing because robots are being put into a lot of other situations as deployment broadens ^[6]. A company statement went further, describing the milestone as progress toward a robot able to do anything that a human can ^[7]. That framing sits uneasily next to the uneven success rates above and next to outside critiques that the demonstrated tasks were trained specifically per-task rather than proven general ^[4]. Public reaction split along similar lines: enthusiasm for concrete dexterity feats like knot-tying and one-handed lightbulb removal was widespread, but a meaningful skeptical thread questioned whether the demos reflect genuine generalization or favorable staging, and argued the humanoid still looks behind rival efforts from Figure, Tesla, and Chinese humanoid robotics programs. Whether 'physical AGI' is a fair label for a system whose hardest dexterity tasks still fail close to half the time is the real open question this launch raises.