Gemini Robotics: Bringing AI into the Physical World (2503.20020v1)

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.

Bringing AI into the Physical World - A Focused Perspective

The paper "Bringing AI into the Physical World" represents a significant advancement in the integration of artificial intelligence with robots. It delineates the development of a family of AI models built upon the Gemini 2.0 foundation, specifically tailored for use in robotics. A notable innovation among these models is the Vision-Language-Action (VLA) generalist model, which bridges the perceptual and interactional gap between robots and their physical environments.

Overview of Embodied Reasoning

Embodied reasoning is pivotal for robots designed to adeptly navigate and manipulate the physical world. To measure progress in embodied reasoning tasks, the researchers introduce ERQA, a benchmark that captures capabilities necessary for embodied interaction. Gemini 2.0, as a Vision-LLM (VLM), significantly advances the state-of-the-art in 2D and 3D understanding, trajectory prediction, and correspondence across multi-view images. The models, including , demonstrate robust performance across diverse embodied reasoning tasks as validated by benchmarking results. The ERQA benchmark itself is introduced as an open-source resource aimed at evaluating and further developing embodied reasoning capabilities for multimodal models.

Vision-Language-Action Model ()

The researchers proceed to introduce , the Vision-Language-Action model, which extends Gemini’s capabilities from perception to action. Designed to control robotic manipulation directly, can proficiently handle a variety of tasks that require dexterous handling, such as folding origami or executing complex card games. This model has been trained in an environment that incorporates vast multimodal datasets and diverse robotic control scenarios, allowing it to effectively generalize across different tasks and environments.

The model's architecture comprises a backbone hosted in the cloud and a local action decoder. This setup addresses traditional latency challenges encountered during real-time robotic control. The , showcased in various tests, retains the embodied reasoning capabilities of Gemini while being capable of performing complex tasks through low-latency, high-frequency control actions across different robot embodiments.

Benchmarking and Generalization

Evaluation on a suite of tasks indicates 's ability to perform intricate manipulation tasks out-of-the-box, displaying proficiency at diverse tasks, from cluttered environments to highly sophisticated long-horizon actions. In comparative evaluations, consistently outperforms baseline models, demonstrating superior capability in handling variations in visual scenes, instructions, and adaptation to new tasks. Such robust generalization is essential for deploying robotics at scale in diverse real-world applications.

Specialization and Safety

The paper explores specialization avenues for , targeting long-horizon tasks and challenging dexterous manipulations. These experiments underline the potential of fine-tuning AI models to achieve state-of-the-art performance in demanding environments. Furthermore, responsible development and safety considerations are thoroughly addressed, focusing on semantic action safety necessitated by robots functioning in unstructured environments. Safety frameworks grounded in Google AI Principles are implemented to ensure responsible deployment and operation.

Implications and Future Work

This research ground lays the foundation for developing generally capable robots that can efficiently and safely interact with the physical world using AI. The potential implications span both practical applications, such as autonomous service robots, and theoretical advancements in robotics and cognitive AI. Future developments will likely focus on extending generalization capabilities even further, harnessing simulation for rich, diverse training environments, and achieving cross-embodiment task adaptation with minimal data requirements.

In conclusion, the integration of robust AI models with real-world embodiment paves the way for potentially transformative deployments of robotics across various domains. As technology progresses, continued emphasis on safety and societal impact will be crucial for guiding responsible advancements.