Google DeepMind Gemini Robotics is a family of AI models designed to help robots understand instructions and act in the physical world. Its vision-language-action (VLA) models turn visual input and instructions into motor commands; its Robotics-ER models reason about the environment and plan what should happen next. The family also includes an on-device model for local inference, and Google has described a Robotics 2 direction that extends to whole-body humanoid control and longer, multi-robot tasks.
What is Google DeepMind Gemini Robotics?
Google DeepMind introduced Gemini Robotics and Gemini Robotics-ER as Gemini 2.0-based models for physical robots. The distinction is their role: Gemini Robotics adds physical actions as an output modality, while Robotics-ER provides embodied reasoning that roboticists can use in programs.
That is a step beyond a chatbot describing an action. A robot has to interpret what it sees, understand what an instruction means in its surroundings, and produce actions that affect the physical world. Google describes this as embodied reasoning: understanding and reacting to the world while acting safely.
Google frames useful robotic capability around three qualities:
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- Generality: adapting to unfamiliar tasks and situations rather than only repeating a narrow script.
- Interactivity: understanding instructions and responding when the environment changes.
- Dexterity: manipulating objects with controlled hand, finger, gripper, or other end-effector movements.
In its 2025 launch material, Google DeepMind said Gemini Robotics more than doubled the performance of other state-of-the-art vision-language-action models on its comprehensive generalization benchmark. That is a vendor-reported result on Google’s benchmark, not a guarantee that a robot will succeed at any arbitrary task.
Can Gemini control a robot?
Yes. Gemini Robotics is designed to produce physical actions for a robot, but it is not a universal controller that can be installed on any machine without integration. A VLA model maps camera or other visual information and instructions into motor commands for a supported robot setup. The robot’s body, sensors, low-level controller, and safety systems still matter.
Google’s launch examples included an ALOHA research robot asked to put pens in a shoe and perform a toy-basketball “slam dunk.” Google said the robot had not previously encountered basketball or that toy and performed the action on its first try. The demonstration illustrates the intended ability to generalize from visual context and language; it is not evidence that all robots or tasks will work first time.
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Google says these models build on Gemini 2.0 through robot-specific fine-tuning. They add physical action to Gemini’s text, video, and audio capabilities, but the robot-specific adaptation and hardware integration are important parts of turning a model into a working system.
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How Gemini Robotics and Robotics-ER fit together
For multi-step work, the reasoning and control roles can be separated. Robotics-ER handles higher-level interpretation and sequencing; the VLA translates instructions about the next action into physical movement. Google illustrates this with waste sorting: the reasoning model can account for local rules and sequence the job, while the VLA carries out the movements.
| Model or approach | Main role | How it is used |
|---|---|---|
| Gemini Robotics (VLA) | Physical control | Uses visual information and instructions to produce motor commands for a robot. |
| Gemini Robotics-ER | Embodied reasoning and planning | Reasons about the physical scene, can call tools such as search or user-defined functions, estimates task progress, and can provide step instructions to a VLA. |
| Gemini Robotics On-Device | Local robotic inference | Runs on the robot rather than depending on a data-network connection for each inference. |
In Google’s description of Robotics 1.5, the VLA turns visual instructions into motor commands, while Robotics-ER 1.5 plans, uses tools, estimates progress, and supports multi-step tasks. Google says the system can “think before taking action” by dividing a longer job into shorter executable segments. It also reports motion transfer across different robot embodiments, meaning skills learned on one robot can transfer to another; that does not mean every skill transfers unchanged to every body.
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What changed with Robotics 1.5 and Robotics 2?
Google’s announcements describe an evolution from robot manipulation and high-level planning toward longer tasks and broader robot bodies. The details below are the capabilities and access Google described in those announcements; they should not be read as a guarantee that each model is generally available today.
| Announcement | What Google described | Access described by Google |
|---|---|---|
| Gemini Robotics and Robotics-ER | Gemini 2.0-based VLA control and embodied spatial reasoning for robotics programs. | Robotics-ER was made available to trusted testers, including Agile Robots, Agility Robots, Boston Dynamics, and Enchanted Tools; Apptronik was named as a humanoid-robot partner. |
| Gemini Robotics 1.5 and Robotics-ER 1.5 | A VLA for motor commands paired with a reasoning model for tools, progress estimation, and multi-step planning. | Robotics-ER 1.5 was offered through the Gemini API and Google AI Studio. Robotics 1.5 was initially limited to select partners. |
| Gemini Robotics 2 and ER 2 | Whole-body humanoid control from feet to fingertips; ER 2 planning for tasks lasting several minutes, progress tracking, self-correction, and coordination of multiple robots. | ER 2 was described as available in Google AI Studio and in private preview on Gemini Enterprise Agent Platform. VLA and on-device models were offered to early-access partners. |
For Robotics 2, Google demonstrated a watering-can task using Apptronik’s Apollo 2 and listed tasks such as tying a knot, sealing a ziplock bag, tool kitting, and precise insertion. This is a broader target than tabletop or upper-body manipulation: it asks a system to coordinate more of a humanoid’s body and sustain plans over a longer period.
What does Gemini Robotics On-Device do?
Gemini Robotics On-Device is designed to run inference locally on a robot. Local operation can reduce dependence on network round trips for latency-sensitive actions and can make a system more useful when connectivity is intermittent or absent. It does not, by itself, establish that every part of a robot’s software or every connected feature works offline.
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Google says the on-device model supports fine-tuning and can adapt to a new task with as few as 50 to 100 demonstrations. The figure is Google’s stated adaptation requirement, not a universal amount for every task, robot, or definition of success. Demonstrated examples included unzipping bags, folding clothes, zipping a lunch box, drawing a card, and pouring salad dressing.
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Google reports that Gemini Robotics 2 achieved 74.2% on general pick-and-place, 78.9% on diverse tool kitting, and 89.6% on precise insertion on the Franka Duo platform. These are vendor-reported demonstration benchmark results for that platform and those tasks. They should not be treated as independently verified results, as a direct comparison with unrelated benchmarks, or as a prediction of performance on another robot.
Google also describes safety as layered rather than as a property of the language model alone. Its approach includes semantic reasoning before action, alignment with Gemini safety policies, and low-level collision-avoidance systems. Google says it uses the ASIMOV benchmark for evaluation. A benchmark or a layered design is not a promise that a physical robot cannot collide with a person or cause damage; deployment still depends on the robot, its operating environment, and its safeguards.
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Is Gemini Robotics available to developers?
Access depends on which model and announcement you mean. Google described Robotics-ER 1.5 as available through the Gemini API and Google AI Studio, while Robotics 1.5 was initially limited to select partners. In the later Robotics 2 announcement, Google said ER 2 was available in Google AI Studio and in private preview on Gemini Enterprise Agent Platform; VLA and on-device models were for early-access partners.
Those statements describe access at the time of the respective announcements, not a promise of current availability, open enrollment, or access in every country. Check Google’s current developer and platform listings for the specific model, region, eligibility, and terms before building against it. The cited announcements do not establish a consumer retail robot or hardware bundle that runs Gemini Robotics.
Who is building with Gemini Robotics?
Google presents the work as an ecosystem effort, not a standalone robot product. Apptronik was named as a humanoid-robot partner. For the original Robotics-ER trusted-tester program, Google named Agile Robots, Agility Robots, Boston Dynamics, and Enchanted Tools. Those names identify partners or testers in the announcements; they do not mean each company sells a generally available Gemini-powered robot.
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