Google DeepMind Ships Three Physical AI Models For Whole Body Control, Dexterity And Multi Robot Collaboration

Google DeepMind has released Gemini Robotics 2, the intelligence layer for its next generation of robots. The release moves the stack past table-top manipulation into whole body control, five finger dexterity and multi robot teamwork. It ships as three separate models with three different access tiers. Most robots today are pre-programmed or tele-operated for narrow, repetitive task sequences. They do not adapt to unpredictable environments, and skills rarely transfer between robot bodies. Gemini Robotics 2 targets all three limits at once. TL;DR Three models ship together: a VLA, an embodied reasoning VLM, and an on-device VLA. One checkpoint drives Apollo 2 with two different hands plus a Franka Duo gripper. Gemini Robotics ER 2 is public preview; the VLA and on-device models stay gated. Multi-finger dexterity is still the weak axis, ranging from 32% to 92%. ASIMOV-Agentic, a new safety benchmark, is on Hugging Face under CC-BY-4.0. 3 Models and What They Do Gemini Robotics 2 Gemini Robotics 2 is the vision-language-action (VLA) model. It converts vision and language input into motor control. It can drive full humanoids from feet to fingertips, and other bi-arm robots. It also handles dexterous manipulation on both multi-finger hands and parallel grippers. Gemini Robotics ER 2 Gemini Robotics ER 2 is the embodied reasoning (ER) model. It is a vision language model that acts as the high level brain. It communicates with humans, understands the physical world, and plans multi-step tasks lasting several minutes. Per its model card, ER 2 is based on Gemini 3.5 Flash. It accepts interleaved text, image, video and audio with a context window of up to 128k, and emits text up to 64K tokens. Gemini Robotics On-Device 2 Gemini Robotics On-Device 2 is the efficient VLA optimized to run locally on the robot. Its model card states it is built on Gemini Robotics 1.5 technology and Google’s on-device Gemma models. Inputs are text, images and robot proprioception as numerical values. Outputs are robot actions as numerical values. The division of labor matters for system design. ER 2 plans and tracks the task, then hands motor execution to a VLA declared as a tool. Developers register low level control interfaces, such as VLA models or navigation APIs, as callable tools. They then stream multimodal video, audio or text directly into the model. Whole body control on Apptronik Apollo 2 Previous Gemini Robotics models controlled only the humanoid’s upper body for table-top tasks. Gemini Robotics 2 extends control to whole body motion for the first time. The worked example uses Apptronik’s Apollo 2. Given the instruction “put the watering can into the green bin in the bottom shelf,” Apollo walks to the table and picks up the watering can. It then takes a few steps to the shelves and places the object at its destination. Google DeepMind is direct about the remaining gap. It states that its robots have more to advance in movement speed. Dexterity across hands and grippers Gemini Robotics 2 can control the five-fingered, 22 degree-of-freedom SharpaWave hand on Apollo 2. Reported actions include tying knots and sealing a ziplock bag. The same model also operates standard two-fingered parallel grippers on a Franka Duo platform for tasks like tight packing. The reported success rates come from one model checkpoint controlling three embodiments: Apollo 2 with SharpaWave hands, Apollo 2 with Inspire hands, and Franka Duo with a Robotiq gripper. CategoryEmbodimentTaskSuccessGeneral whole body manipulationApollo 2 + Inspire handsPick up from shelf76.3%General whole body manipulationApollo 2 + Inspire handsPick up from table68.4%General whole body manipulationApollo 2 + Inspire handsPick up from floor45.7%Multi-finger dexterityApollo 2 + Sharpa handsUnscrew bulb92%Multi-finger dexterityApollo 2 + Sharpa handsTie trash bag44%Multi-finger dexterityApollo 2 +