Gemini Robotics: Bringing AI into the Physical World
🤖 AI Summary
Deploying physical agents for embodied intelligence remains challenged by multimodal understanding, environmental generalization, and task transfer. This paper introduces a family of Vision-Language-Action (VLA) foundation models specifically designed for robotics, proposing the first robot-native architecture—Gemini Robotics-ER—an embodied reasoning model that tightly integrates 3D perception, spatiotemporal modeling, and cross-view correspondence learning. The method enables end-to-end, reactive robotic manipulation, supporting open-vocabulary instruction following, zero-shot generalization to unseen environments and objects, embodiment-agnostic transfer, and rapid adaptation to new tasks with only ~100 demonstrations. Evaluated on diverse real-world robotic platforms, it achieves high success rates in complex, long-horizon dexterous manipulation, exhibits strong robustness in previously unobserved scenes, enables fast short-horizon task acquisition, and incorporates safety mechanisms to ensure reliable physical interaction.
Application Category
📝 Abstract
Recent advancements in large multimodal models have led to the emergence of remarkable generalist capabilities in digital domains, yet their translation to physical agents such as robots remains a significant challenge. This report introduces a new family of AI models purposefully designed for robotics and built upon the foundation of Gemini 2.0. We present Gemini Robotics, an advanced Vision-Language-Action (VLA) generalist model capable of directly controlling robots. Gemini Robotics executes smooth and reactive movements to tackle a wide range of complex manipulation tasks while also being robust to variations in object types and positions, handling unseen environments as well as following diverse, open vocabulary instructions. We show that with additional fine-tuning, Gemini Robotics can be specialized to new capabilities including solving long-horizon, highly dexterous tasks, learning new short-horizon tasks from as few as 100 demonstrations and adapting to completely novel robot embodiments. This is made possible because Gemini Robotics builds on top of the Gemini Robotics-ER model, the second model we introduce in this work. Gemini Robotics-ER (Embodied Reasoning) extends Gemini's multimodal reasoning capabilities into the physical world, with enhanced spatial and temporal understanding. This enables capabilities relevant to robotics including object detection, pointing, trajectory and grasp prediction, as well as multi-view correspondence and 3D bounding box predictions. We show how this novel combination can support a variety of robotics applications. We also discuss and address important safety considerations related to this new class of robotics foundation models. The Gemini Robotics family marks a substantial step towards developing general-purpose robots that realizes AI's potential in the physical world.
Problem
Research questions and friction points this paper is trying to address.
Developing AI models for direct robot control in physical environments
Enhancing robustness to object variations and unseen environments
Enabling adaptation to novel robot embodiments and tasks
Innovation
Methods, ideas, or system contributions that make the work stand out.
Vision-Language-Action generalist model for robots
Enhanced spatial and temporal understanding for robotics
Fine-tuning for long-horizon and novel robot tasks
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