Gemini Robotics-ER 1.6: Powering real-world robotics tasks through enhanced embodied reasoning

For robots to be truly helpful in our daily lives and industries, they must do more than follow instructions, they must reason about the physical world. From navigating a complex facility to interpreting the needle on a pressure gauge, a robot’s “embodied reasoning” is what allows it to bridge the gap between digital intelligence and physical action.

Today, we’re introducing Gemini Robotics-ER 1.6 , a significant upgrade to our reasoning-first model that enables robots to understand their environments with unprecedented precision. By enhancing spatial reasoning and multi-view understanding, we are bringing a new level of autonomy to the next generation of physical agents.

This model specializes in reasoning capabilities critical for robotics, including visual and spatial understanding, task planning and success detection. It acts as the high-level reasoning model for a robot, capable of executing tasks by natively calling tools like Google Search to find information, vision-language-action models (VLAs) or any other third-party user-defined functions.

To understand a key strength of Gemini Robotics-ER 1.6, we must look at how it combines capabilities like spatial reasoning and world knowledge to solve complex, real-world problems. A perfect example is instrument reading.

Capabilities like instrument reading and more reliable task reasoning will enable Spot to see, understand, and react to real-world challenges completely autonomously.

Figure 1: Benchmark results comparing Gemini Robotics-ER 1.6 with Gemini Robotics-ER 1.5 and Gemini 3.0 Flash models. The instrument reading evaluations were run with agentic vision enabled (except for Gemini Robotics-ER 1.5 which doesn’t support it). All other evals were run with agentic vision disabled. The single view and multiview success detection evaluations contain different examples so are not comparable.

Gemini Robotics-ER 1.6 shows significant improvement over both Gemini Robotics-ER 1.5 and Gemini 3.0 Flash , specifically enhancing spatial and physical reasoning capabilities such as pointing, counting, and success detection. We are also unlocking a new capability: instrument reading, enabling robots to read complex gauges and sight glasses — a use case we discovered through close collaboration with our partner, Boston Dynamics.

Starting today, Gemini Robotics-ER 1.6 is available to developers via the Gemini API and Google AI Studio . To help you get started, we are sharing a developer Colab containing examples of how to configure the model and prompt it for embodied reasoning tasks.

Pointing is a fundamental capability for an embodied reasoning model, evolving with each model generation. Points can be used to express many concepts, including: