Google DeepMind unveils Gemini Robotics-ER 1.6 as robot dogs gain precision instrument reading

Robot dogs now read gauges and thermometers using Google Gemini

Robots such as Boston Dynamic's four-legged Spot can now accurately read analog thermometers and pressure gauges while roaming around factories and warehouses. Those improvements come courtesy of Google DeepMind's newest robotic AI model that aims to enhance robotic capabilities for 'embodied reasoning' when interacting with physical environments. The new Gemini Robotics-ER 1.6 model announced on April 14 performs as a "high-level reasoning model for a robot" that can plan and execute tasks, according to Google DeepMind. This model also unlocks the capability of accurately reading instruments such as complex gauges and doing visual inspections using sight glasses that provide a transparent window to peek inside tanks and pipes -- a performance upgrade that came about through Google DeepMind's ongoing collaboration with robotics company Boston Dynamics. Boston Dynamics has a keen interest in testing both quadruped and humanoid robotic workers in a wide range of industrial facilities, including the automotive factories of the robotic company's corporate owner, Hyundai Motor Group. The company's robot "dog," Spot, is being trialled as a robotic inspector that roams throughout industrial facilities to check up on everything. Such inspection duties require "complex visual reasoning" to interpret the multiple needles, liquid levels, container boundaries and tick marks, along with text, in various instruments. The model driving it To handle such tasks, the Gemini Robotics-ER 1.6 model provides robots with "agentic vision" that combines visual reasoning with the capability of executing code to create a "visual scratchpad" for inspecting and manipulating images. Such agentic vision was first introduced in Google's Gemini 3.0 Flash model back in January 2026. The agentic vision capability reportedly helps to boost robotic performance on instrument reading tasks from 23 percent in the older Gemini Robotics-ER 1.5 model to 98 percent in the new Gemini Robotics-ER 1.6 model. For the sake of comparison, Gemini 3.0 Flash delivered just 67 percent accuracy. The baseline Gemini Robotics-ER 1.6 model can still achieve 86 percent accuracy in reading instruments even without agentic vision. That is because the model uses a process of pointing to different elements in a visual image to process complex tasks, such as counting the number of items or identifying the most salient features. It also supposedly delivers an improved "multi-view reasoning" capability that allows a robotic system to use multiple camera streams to better understand its environment. One performance example given by Google DeepMind highlights how Gemini Robotics-ER 1.6 could correctly identify the number of hammers, scissors, paintbrushes, pliers, and various gardening tools in a cluttered image. By comparison, the older Gemini Robotics-ER 1.5 model failed to accurately count the number of hammers or paint brushes, completely overlooked the scissors, and falsely identified a nonexistent wheelbarrow because that was one of the requested items in the identification task. That implies the newer model has less of a "hallucination" problem than the older one, even if the latest model is still far from achieving human-level comprehension of its surroundings. Google also describes Gemini Robotics-ER 1.6 as its "safest robotics model yet," with a "substantially improved capacity to adhere to physical safety constraints." It enables robots to both follow safety instructions and to make safer decisions when handling liquids or materials. The new model can also more accurately perceive the risk of injury to humans in different scenarios, such as a young child sticking something into an electrical socket. Future application The practical test of this model's value will come as robotics companies and researchers get more hands-on time to test its capabilities. So far, robots have proven most efficient and productive when performing as highly specialized machines doing the same specific tasks over and over again in factory assembly lines or performing highly coordinated and choreographed movements in warehouse aisles. Companies like Google are betting that the latest AI models can help robots become more free-range workers operating in complex and less controlled real-world environments -- a prospect that also carries the greater risk of robots doing damage or harm to humans if something goes wrong. At the very least, the latest model may nudge us one step closer to a future where a General Atomics International Mark 4 robot can scan the room and correctly exclaim, "There's no fudge here!"

DeepMind launches Gemini Robotics-ER 1.6 to meet precise physical AI demands - SiliconANGLE

DeepMind launches Gemini Robotics-ER 1.6 to meet precise physical AI demands Google DeepMind, Alphabet Inc.'s artificial intelligence research division, introduced a new foundation robotics artificial intelligence model on Tuesday, designed as a significant upgrade for understanding and precise spatial reasoning. The new model, named Gemini Robotics-ER 1.6 from Gemini Robotics, enhances spatial reasoning and multi-view understanding to bring greater autonomy to physical agents and robots of all kinds. DeepMind said this model provides high-level reasoning capabilities for robotics, offering a layer for task planning and tool calling. These include native tools for Google Search to find information, vision-language-action models, and other third-party user-defined functions to extend capabilities. Examples of improvements include precision object detection, categorization and detection - a must for robots when picking out and picking up items - especially for sorting parcels or cleaning up a messy room. Also in relational logic, such as making comparisons, for example, identifying the smallest object in a set, or defining from-to relationships when moving object X to location Y. This is coupled with enhancements to trajectory mapping and defining the best way to grab objects. The company also said the model works well within constraints and reasons through complex prompts like "point to every object small enough to fit inside the blue cup." Beyond making robots move, DeepMind researchers also boosted the model's ability to understand and read things like gauges and instruments - this requires complex visual reasoning. It's also fundamental for operating within environments such as factories, warehouses and even domestic spaces such as houses. In many cases, gauges include needles, tick marks, finely etched numbers and more indicators (and sometimes instructions) that need to be resolved to fully understand the nature of the readings. "Capabilities like instrument reading and more reliable task reasoning will enable Spot to see, understand, and react to real-world challenges completely autonomously," said Marco da Silva, vice president and general manager of Spot at Boston Dynamics Inc., the dog-like robot the company develops. DeepMind said the Robotics-ER 1.6 achieves this level of accuracy via agentic vision, which combines visual reasoning with code execution. The model takes a snapshot of the image, resolving the fine details and then uses carefully curated code to estimate the proportions and intervals to get an accurate reading, then finally uses its reasoning engine to interpret the reading.