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AI foundation models give robots the ability to see, reason, and act in the physical world

AI foundation models give robots the ability to see, reason, and act in the physical world

New Capabilities

Google DeepMind's Gemini Robotics-ER 1.6 marks the latest advance in a rapid convergence of large language models and physical robotics

April 14th, 2026: Google DeepMind releases Gemini Robotics-ER 1.6

Overview

For decades, industrial robots have been powerful but rigid. Google DeepMind's Gemini Robotics-ER 1.6, released April 14, 2026, is the latest version of a new approach: it gives robots the flexible reasoning that powers chatbots, tailored to understand and act in physical space. Paired with an agentic vision system, it reads pressure gauges, thermometers, and digital readouts with 98% accuracy—a capability that emerged from collaboration with Boston Dynamics, which deploys the Spot robot in industrial facilities.

This release is part of a broader race to build what the industry calls 'physical AI'—foundation models that let any robot understand novel environments without explicit programming. Google DeepMind, NVIDIA, and a growing roster of Chinese manufacturers are converging on the same bet: that the architectures behind large language models can be adapted to give robots general-purpose intelligence. The humanoid robot market is projected to grow from $2 billion in 2024 to over $13 billion by 2029, and the companies that control the underlying AI models will shape which robots can do what.

Why it matters

The AI models that power chatbots can now control physical robots.

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Key Indicators

98%
Gauge-reading accuracy
Gemini Robotics-ER 1.6 with agentic vision achieves 98% accuracy reading industrial instruments, up from 23% in the previous version
$13B+
Projected humanoid robot market by 2029
The market is expected to grow from $2 billion in 2024 at a 45% compound annual growth rate
13,317
Humanoid robots shipped globally in 2025
Chinese manufacturers claimed 87% of unit volume, led by Unitree Robotics with over 5,500 units
30,000/yr
Boston Dynamics Atlas production target
Factory-scale production target for the Atlas humanoid robot, powered by Gemini Robotics foundation models

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People Involved

Organizations Involved

Timeline

July 2023 April 2026

10 events Latest: April 14th, 2026 · 2 months ago
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  1. Google DeepMind releases Gemini Robotics-ER 1.6

    Latest Release

    The new model significantly improves spatial and physical reasoning—pointing, counting, and success detection—and introduces instrument reading, enabling robots to autonomously read pressure gauges, thermometers, and digital displays with up to 98% accuracy using agentic vision. Available immediately via the Gemini API and Google AI Studio.

  2. Hassabis predicts robotics "ChatGPT moment" at Davos

    Statement

    Google DeepMind's CEO told the World Economic Forum that the field would witness a breakthrough in physical intelligence within 18 months, comparable to the public impact of ChatGPT.

  3. Boston Dynamics and Google DeepMind announce partnership at CES 2026

    Partnership

    At Hyundai's CES press conference, the two companies revealed plans to integrate Gemini Robotics foundation models into both the Spot quadruped and the next-generation Atlas humanoid, with a factory production target of 30,000 Atlas units per year.

  4. Gemini Robotics 1.5 released with expanded capabilities

    Release

    The updated model improved spatial understanding, natural language interaction, and multi-step task planning, and added the ability to call external tools like Google Search during operation.

  5. Gemini Robotics On-Device released for low-latency local control

    Release

    A lightweight version of Gemini Robotics optimized to run directly on robot hardware, enabling real-time dexterous tasks like unzipping bags and folding clothes without cloud connectivity.

  6. Google DeepMind introduces Gemini Robotics and Gemini Robotics-ER

    Release

    DeepMind debuted two models built on Gemini 2.0: Gemini Robotics, a full VLA model that directly controls robots, and Gemini Robotics-ER, a reasoning-focused model for environmental understanding and task planning.

  7. Boston Dynamics retires hydraulic Atlas, unveils electric version

    Hardware

    Boston Dynamics replaced its famous hydraulic humanoid with an all-electric design built for commercial manufacturing, signaling a shift from research spectacle to factory-floor deployment.

  8. NVIDIA announces Project GR00T for humanoid robots

    Competition

    NVIDIA entered the robotics foundation model race, announcing a dedicated platform for training humanoid robot AI alongside major hardware and simulation investments.

  9. DeepMind publishes Robot Constitution safety framework

    Safety

    As part of its AutoRT system, DeepMind introduced a set of safety rules governing AI-controlled robots, inspired by Asimov's laws but designed for real-world deployment constraints.

  10. Google DeepMind releases RT-2, the first vision-language-action model

    Research

    RT-2 demonstrated that a robot could benefit from training on internet-scale text and image data, translating web knowledge into physical actions. It established the vision-language-action (VLA) paradigm that all subsequent robotics foundation models have followed.

Historical Context

3 moments from history that rhyme with this story — and how they unfolded.

1961

Unimate and the birth of industrial robotics (1961)

George Devol's Unimate, a 4,000-pound robotic arm, was installed at General Motors' die-casting plant in Ewing, New Jersey—the first industrial robot deployed in a factory. It performed a single task: lifting and stacking hot metal parts that were dangerous for human workers. The machine cost the equivalent of $500,000 in today's dollars and could only follow pre-programmed magnetic drum instructions.

Then

GM expanded robotic welding across its plants within five years. By 1969, the Stanford Arm introduced computer-controlled articulation, making robots more flexible.

Now

Industrial robots became foundational to manufacturing. By 2024, over 4 million industrial robots were operating worldwide, but nearly all still required explicit programming for each specific task.

Why this matters now

Gemini Robotics-ER 1.6 represents the next transition: from robots that execute pre-programmed instructions to robots that reason about novel situations. Just as Unimate proved machines could handle dangerous factory work, foundation model robots are proving machines can handle unpredictable factory environments.

October 2012

ImageNet and the deep learning revolution (2012)

Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton entered a deep neural network called AlexNet into the ImageNet Large Scale Visual Recognition Challenge and crushed the competition, reducing image classification error from 26% to 16%. The result stunned the computer vision community and triggered a rush to apply deep learning to every perceptual task.

Then

Within two years, every major tech company had launched or acquired a deep learning research lab. GPU maker NVIDIA's stock began a decade-long ascent.

Now

The ImageNet moment proved that scale plus data plus compute could solve problems that hand-engineered approaches had struggled with for decades. It directly led to the transformer architectures behind today's language models and, now, robotics foundation models.

Why this matters now

Demis Hassabis has explicitly compared the current state of robotics AI to the period just before deep learning's breakout. The pattern is similar: a general-purpose architecture (transformers/VLAs) is being applied to a domain (physical robotics) that previously required domain-specific engineering. Gemini Robotics-ER 1.6's jump from 23% to 98% instrument-reading accuracy echoes the kind of sudden capability gains that AlexNet delivered for image recognition.

March 2012 - 2015

Kiva Systems and warehouse automation (2012-2015)

Amazon acquired Kiva Systems for $775 million in March 2012, gaining a fleet of small orange robots that could autonomously navigate warehouse floors to bring shelving units to human pickers. Amazon rebranded the division as Amazon Robotics and stopped selling the technology to competitors, forcing rivals like Walmart and FedEx to develop or acquire their own solutions.

Then

Amazon deployed over 200,000 Kiva robots across its fulfillment centers within three years, significantly reducing order processing time.

Now

Amazon's robot acquisition triggered a warehouse automation arms race. By 2026, the warehouse robotics market exceeded $9 billion annually, and companies like Agility Robotics were deploying humanoid robots in warehouse settings.

Why this matters now

The Kiva acquisition showed how a single company's AI robotics investment could reshape an entire industry's competitive dynamics. The Google DeepMind–Boston Dynamics partnership carries similar potential: if Gemini-powered robots prove significantly more capable than competitors, the companies that control the foundation models gain outsized influence over industrial automation.

Sources

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