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Gemini Robotics-ER

Gemini Robotics 1.5

Powering an era of physical agents to transform how robots actively understand their environments

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Gemini Robotics models allow robots of any shape and size to perceive, reason, use tools and interact with humans. They can solve a wide range of complex real-world tasks – even those they haven’t been trained to complete.

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Gemini Robotics 1.5 is designed to reason through multi-step complex tasks, and to make decisions to form a plan of action. It will then work to carry out each step autonomously.

Capabilities

Gemini models are capable of responding to text, images, audio, and video. Gemini Robotics adds the ability to reason about physical spaces – allowing robots to take action in the real world.

  • Generality

    Understands the physical world, and adapts and generalizes its behaviour to fit new situations. Breaks down goals into manageable steps to make longer-term plans and overcome unexpected problems.

  • Agentic

    Assess complex challenges, natively call tools – like Google Search – to look up information, and create detailed step-by-step plans to overcome them.

  • Thinking

    Enables robots to think before acting, improving the quality of their actions, and making their decisions more transparent in natural language.

  • Interactivity

    Understands and responds to everyday commands. Can explain its approach while taking action. Users can redirect it at any point, without using technical language. It also adjusts to any changes in its environment.

  • Dexterity

    Enables robots to tackle complex tasks requiring fine motor skills and precise manipulation – like folding origami, packing a lunch box, or preparing a salad.

  • Multiple embodiments

    Adapts to a diverse array of robot forms, from bi-arm static robotic platforms like ALOHA and Bi-arm Franka, to humanoid robots like Apptronik’s Apollo. A single model can be used across all these robots, in turn accelerating its learning across multiple embodiments.

Hands-on

See how Gemini Robotics handles a range of different tasks.

Agentic capabilities

Uses digital tools autonomously to solve complex tasks.

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Thinking while acting

Solves longer, multi-step tasks – without needing new instructions after each step.

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Learning across embodiments

Transfers learned motions across robots of different sizes and shapes, helping robots to become more useful.

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Embodied reasoning

Understand its environment and how to complete a task.

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Generality in action

Generalizes across novel situations and solves a vast range of tasks.

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Dynamic interactions

Responds to natural conversation and adapts rapidly to changing environments.

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Partnering with Apptronik

Helping to build the next generation of humanoid robots.

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Dexterous skills

Perform tasks that require fine motor skills and coordination.

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Responsibly advancing AI and robotics

To ensure Gemini Robotics benefits humanity, we’ve taken a comprehensive approach to safety, from practical safeguards to collaborations with experts, policymakers, and our Responsibility and Safety Council.

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Model and tools

We take a dual-model approach, pairing a vision-language-action (VLA) with an embodied reasoning (ER) model. Each model plays a specialized role, working together as a powerful and versatile system.

Gemini Robotics SDK

Helping developers easily adapt our Gemini Robotics On-Device model to new tasks and environments.

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Collaborations

We’re partnering with Apptronik to build the next generation of humanoid robots. We’re also working with over 60 trusted testers to guide the future of Gemini Robotics-ER.

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Gemini Robotics

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Gemini Robotics-ER

Gemini Robotics-ER 1.5

Our state-of-the-art embodied reasoning model – it specializes in understanding physical spaces, planning, and making logical decisions within its surroundings

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Our Gemini-based multimodal model gives advanced world understanding to robots.

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Capabilities

Gemini Robotics-ER 1.5 is capable of making detailed plans from simple commands. For example, let’s say a human instructed it to ‘clean the kitchen’. The ER model would break down the task into smaller, manageable steps – clear the counter, load the dishwasher, wipe the surfaces. This model also supports thinking.

  • Orchestration

    Orchestrates robot activities, like a high-level brain. Excels at planning and making logical decisions within a physical environment. Interacts in natural language, estimates progress, and can natively call tools – like using Google Search to look for information.

  • Advanced spatial understanding

    Perceives and understands the surrounding environment to locate and handle objects with greater accuracy.

  • Temporal reasoning

    Understands the cause and effect relationships between objects and actions as they unfold over time.

Benchmarks

Aggregated performance on 15 embodied reasoning academic benchmarks. The benchmarks include: Point-Bench, RefSpatial, RoboSpatial-Pointing, Where2Place, BLINK, CV-Bench, ERQA, EmbSpatial, MindCube, RoboSpatial-VQA, SAT, Cosmos-Reason1, Min Video Pairs, OpenEQA and VSI-Bench.

Model information

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Supported data types for input Image, Video, Text
Supported data types for output Text
Supported # tokens for input 1M
Knowledge cutoff January 2025
Availability Public preview