Gemini Robotics-ER 1.6Translation site

What is Gemini Robotics-ER 1.6?

Gemini Robotics-ER 1.6 is an official Google DeepMind launch on April 15, 2026 designed for autonomous robots in physical environmentsfigurative reasoningAI Modeling. As a major upgrade to the Gemini Robotics-ER series, version 1.6 focuses on Embodied Reasoning, which enables robots to interpret visual inputs, plan tasks, and determine whether a task has been completed or not, marking a shift from command-following to context-aware decision-making.

Key Features of Gemini Robotics-ER 1.6

1. inference ability::
   • Mission planning and decision-making: As a high-level reasoning model for robots, Gemini Robotics-ER 1.6 is responsible for task planning, tool invocation, and success detection, and does not directly manipulate mechanical actions.
   • Visual and spatial understanding: Significantly improves accuracy in object recognition, counting, and spatial relationship judgments. Ability to recognize tools scattered on a workbench, count accurately, and point to objects as part of the reasoning process.
   • Multi-view task completion validation: Fusing real-time images from different cameras to form a coherent understanding of the scene, cross-validating task results from multiple perspectives, and avoiding blind spots from a single viewpoint.
2. Autonomous meter reading::
   • Industrial Instrument Identification: The addition of instrumentation reading capabilities enables the robot to read devices such as complex pressure gauges, liquid level meters and digital displays. By combining visual inference with code execution, the robot can zoom in and out of images, recognize pointers and scale marks, and compute values with extreme accuracy.
   • Automatic camera distortion correction: The model automatically writes code to correct for camera aberrations (e.g., barrel or pincushion effects in wide-angle lenses), calculates scale marks with sub-millimeter accuracy, and adapts to different camera settings without extensive manual reprogramming.
3. Upgrade of security capabilities::
   • Physical constraints understanding: The model understands the physical constraints of executing commands and explicitly avoids unsafe items such as liquids and objects weighing more than 20 kilograms.
   • Risk identification and prevention: The ability to detect the risk of human injuries in video is 10% higher than its predecessor, reinforcing the safety boundaries of robot planning and sensing, bringing higher compliance and lower accident rates to scenarios such as warehouse logistics and medical assistance.

Scenarios for Gemini Robotics-ER 1.6

1. industrial automation::
   • Equipment Inspection: In high-risk environments such as manufacturing, oil and gas, refineries, and energy facilities, robots can autonomously read traditional analog gauges to enable autonomous upgrades of stock plants.
   • quality control: Through high-precision visual reasoning, the robot can detect product defects and ensure production quality.
2. Warehouse Logistics::
   • Cargo handling and sorting: Robots can understand the warehouse environment, autonomously plan handling paths, avoid collision with unsafe items, and improve logistics efficiency.
   • stocktaking: Through object recognition and counting functions, the robot can autonomously perform inventory counting tasks.
3. medical assisting::
   • patient care: The robot can assist healthcare workers in tasks such as patient handling and drug distribution, reducing the burden of manpower and the risk of cross-infection.
   • surgical aid: In the operating room, the robot can autonomously read medical device meters to provide real-time data support for doctors.

How to use Gemini Robotics-ER 1.6?

1. Developer Access::
   • Gemini Robotics-ER 1.6 is now available to developers via the Gemini API and Google AI Studio. Developers can call the API to integrate the model into their own robotic systems.
   • DeepMind also released the Colab a type of literature consisting mainly of short sketchesThis, provides configuration examples and hint guides for embodied reasoning tasks to help developers get started quickly.
2. Robot Integration::
   • Boston Dynamics has integrated Gemini Robotics-ER 1.6 into the AIVI-Learning platform for its Spot quadruped robot. With Gemini's inference capabilities, Spot can now automate complex visual inspection tasks such as 5S compliance audits, level measurement, pallet counting and floor water detection.
   • The integrated system supports “zero downtime upgrades,” with DeepMind continuously optimizing the model in the cloud, so customers' inspection accuracy is automatically improved without the need for manual updates or scheduled downtime.
3. Custom Function Development::
   • Developers can customize the functionality and behavior of the model according to specific application scenarios. For example, develop specific meter reading algorithms, task planning strategies, or security constraint rules.

Product Comparison

1. Comparison with previous version::
   • Gemini Robotics-ER 1.5: Version 1.6 surpasses version 1.5 in spatial and physical reasoning capabilities across the board, especially in point location, counting, and task success determination. Gauge reading accuracy jumps from 23% in version 1.5 to 93%.
   • Gemini 3.0 Flash: Version 1.6 outperforms Gemini 3.0 Flash in visual and spatial understanding, task planning, and task completion judgments. 1.6 achieves a success rate of 931 TP4T on the meter reading task, compared to 671 TP4T for Gemini 3.0 Flash.
2. Comparison with other robot models::
   • Alibaba RynnBrain: While RynnBrain performs well in some of the benchmarks, Gemini Robotics-ER 1.6 has significant advantages in embodied reasoning, industrial meter recognition, and safety capabilities. In particular, on the instrumentation reading task, version 1.6 has a much higher success rate than the other models.
   • (sth. or sb) elseOpen SourceRobot Model: Gemini Robotics-ER 1.6 provides more comprehensive feature support and higher performance compared to open source models. Its native invocation of Google Search, VLA, etc. enables the robot to obtain richer information support and improve the accuracy and efficiency of task execution.