Earlier this year, we introduced Gemini Robotics 2 — our next-generation intelligence layer enabling robots to think, act, and adapt in the physical world. At the core of this breakthrough is Gemini Robotics Embodied Reasoning 2 (ER 2), a specialized Gemini model that serves as a robot’s high-level brain. Working across any robot embodiment — from stationary arms to full humanoids — Gemini Robotics ER 2 combines advanced spatial and temporal video understanding with long-horizon planning and built-in safety guardrails. This enables robots to converse naturally with humans, orchestrate low-level action models and hardware APIs, execute multi-minute tasks involving hundreds of decisions, and collaborate across multi-robot teams.
Today, we are bringing this same frontier embodied reasoning model to enterprise production environments: Gemini Robotics ER 2 is now available in public preview on the Gemini platform. For the first time, organizations can pair Google DeepMind’s latest embodied reasoning model for robotics with the enterprise-grade security, multi-agent orchestration, and end-to-end lifecycle management of Google Cloud — making it easier to simulate, evaluate, and deploy safe, adaptive physical AI at scale.
Powering physical agents for enterprise robotics
Gemini Robotics ER 2 transforms a robot into a physical agent. The model combines deep spatio-temporal reasoning and long-horizon planning, enabling robots to orchestrate steps to complete a task, self-correct, and generalize to novel situations. To build an agentic setup, developers can declare low-level control interfaces — like Vision-Language-Action (VLA) models or navigation APIs — as tools, and stream multimodal video, audio, or text directly into the model.
Here is what the model delivers for businesses building and operating robotics:
1. Physical task verification, understanding, and self-correction
In physical operations, a robot needs to know whether a step actually succeeded before moving on to the next one. Gemini Robotics ER 2 adapts to new and unfamiliar situations on the fly by reasoning over continuous video feeds. The model:
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Classifies task progress into binary status, discrete categories, or continuous scores to improve real-time situational awareness and enable real-time adjustment or iteration on failed steps, without restarting an entire workflow.
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Pinpoints the exact video frame where a critical event occurs (such as when to stop pouring a liquid) with 91.3% accuracy and a 0.96-second mean absolute frame distance, running at 4x the execution speed and a fraction of the compute cost of many larger model categories.
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It advances our core spatial reasoning capability, including visual question answering and instrument reading. For example, it can read and interpret values across 10+ instrument categories including circular dials, sight glasses, digital displays, linear scales, rulers, and liquid thermometers directly from visual input.
2. One intelligence layer across any embodiments
Gemini Robotics ER 2 orchestrates robots of many shapes and sizes, scaling its intelligence from stationary arms and wheeled rovers to quadrupeds and complex forms. It also unlocks multi-robot collaboration, allowing different types of robots to share a semantic understanding of their workspace, divide labor, and hand off tasks to complete workflows a single machine could not do alone.
3. Agentic tool orchestration and natural language interaction
Gemini Robotics ER 2 thinks about what comes next while simultaneously orchestrating actions. Developers can declare low-level control interfaces — such as VLA models, navigation APIs, or custom functions — as tools across simulation, real-world robot control, or human tele-operation. Gemini Robotics ER 2 can orchestrate navigation and manipulator movement APIs from natural language commands, allowing the robot to explain its approach while performing an action, and letting operators redirect it without using technical language.
4. Build safe, adaptive physical agents in Google Cloud
Developers and enterprise teams use the Gemini platform to simulate, orchestrate, and manage physical agents, which are then deployed to run directly on physical robots at the edge using Google Cloud’s scalable infrastructure:
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Simulate fast: Test robot behaviors before touching hardware by running simulations in familiar environments like MuJoCo-Warp and NVIDIA Isaac Sim. Then generate synthetic scenarios with multimodal models and auto-label workcell imagery on the Gemini platform.
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Train smart: Use Gemini Robotics ER 2’s spatial pointing, moment finding, and progress classification to curate and label demonstration videos in Cloud Storage and BigQuery. Then train custom Vision-Language-Action (VLA) models through the Gemini platform, GKE, or Cluster Director.
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Operate easily: Deploy Gemini Robotics ER 2 workflows on the Gemini platform with enterprise IAM, security controls, and built-in function calling. Offload latency-sensitive local workloads like SLAM and path planning to factory floors with Google Distributed Cloud, and stream fleet telemetry into BigQuery. Explore the full architecture in Google Cloud Physical AI solutions.
How customers are building with Gemini Robotics ER 2 in Gemini platform
“We’ve seen exponential growth in customers using AIVI-Learning every month, and Gemini Robotics ER 2 brings them even more value. Video changes what an inspection can be. Spot isn’t just checking whether something looks right in a moment in time, the ability to apply AI models to video opens a whole new world of possibilities.”
Marco da Silva, VP and General Manager of Spot at Boston Dynamics
“DeepReach AI is the real-world skills engine for physical AI. Our hardware and AI platform capture how skilled people work all over the world, and turn it into data flywheel for model training. Gemini Robotics ER 2 gives us accurate task understanding of every recording, to help us find data that matters most — faster and at a far greater scale.”
Chris Liu, Co-founder and CTO at DeepReach
“Advanced reasoning models have the potential to help robots interpret complex environments, plan actions and adapt to changing conditions, opening new possibilities for physical AI. Google’s Gemini Robotics ER 2 model represents an important step in this direction, and we’re excited to be working with Google on exploring how these emerging capabilities can shape the next generation of robotics compute platforms.”
Barrie Mullins, Senior Director of Arm Total Design for Physical AI, at Arm
“We aim to accelerate materials R&D workflows so our scientists can test more ideas, faster. Most lab automation today relies on rigid scripts for single, isolated tasks. By using Gemini Robotics ER 2, we are giving our robots the visual and physical flexibility to work with our existing infrastructure – whether that’s reading a touchscreen on a legacy microwave reactor that has no API, balancing and loading a centrifuge, or moving samples safely through a busy lab. This is the next level that we aspire to reach. This brings us much closer to self-driving labs that can run full experimental campaigns from synthesis to separation, freeing our researchers to focus on thinking up new ideas for discovering new materials.”
Prof. Loh Xian Jun, Executive Director at ASTAR Institute of Materials Research and Engineering (ASTAR IMRE) and Executive Director of MARVEL-AI”
Get started with Gemini Robotics ER 2 in Gemini platform
Developers and enterprise teams can start building with Gemini Robotics ER 2 today in preview via Model Garden in Gemini platform.
