In recent years, artificial intelligence (AI) has made significant advances in a variety of fields, including natural language processing (NLP) and computer vision. However, one of the major challenges of AI is integration into the physical world. AI is excellent at inferring and solving complex problems, but these achievements are primarily limited to digital environments. To enable AI to perform physical tasks through robotics, it requires a deeper understanding of spatial reasoning, object manipulation, and decision-making. To address this challenge, Google has introduced Gemini Robotics, a set of models that were intentionally developed for robotics and embodied AI. Built on Gemini 2.0, these AI models integrate advanced AI inference with the physical world to enable robots to perform a wide range of complex tasks.
Understanding Gemini Robotics
Gemini Robotics is a pair of AI models built on the foundations of Gemini 2.0, a cutting-edge vision language model (VLM) that can process text, images, audio and video. Gemini Robotics is essentially an extension of VLM to the Vision-Language-action (VLA) model. This allows Gemini models to understand and interpret visual inputs and process natural language instructions, as well as carry out physical actions in the real world. This combination is important for robotics, allowing machines to “see” the environment, as well as understand it in the context of human language, and perform the complex nature of real tasks, from simple object manipulation to more complex, dexterous activities.
One of the key strengths of Gemini Robotics is its ability to generalize through a variety of tasks without the need for extensive retraining. This model can follow open lexical instructions, tailor it to variations in the environment, and even handle unexpected tasks that are not part of the initial training data. This is especially important for creating robots that can operate in dynamic and unpredictable environments such as homes and industrial environments.
Essentialized reasoning
A key challenge in robotics has always been the gap between digital inference and physical interaction. Humans can easily understand complex spatial relationships and interact seamlessly with their surroundings, but robots struggle to replicate these abilities. For example, robots are limited in their understanding of spatial dynamics, adapting to new situations, and handling unpredictable real-world interactions. To address these challenges, Gemini Robotics incorporates “embodied reasoning.” This is a process in which systems can understand and interact with the physical world in a similar way to how humans do it.
In contrast to AI inference in a digital environment, embodied inference includes several key components, including:
- Object detection and manipulation: Equivalent inference allows Gemini Robotics to discover and identify objects in their environment, even if they were not previously seen. It can grasp objects, predict where to determine their state, and perform movements such as pulling out, pouring liquids, folding paper and more.
- Trajectory and understanding prediction: Equipped inference allows Gemini Robotics to predict the most efficient path for movement and identify the best points to hold objects. This ability is essential for tasks that require accuracy.
- 3D Understanding: Essentialized reasoning allows robots to recognize and understand three-dimensional space. This ability is especially important for tasks that require complex spatial manipulation, such as folding and object assembly. Understanding 3D, robots are superior for multiview 3D support and tasks with 3D bounding box prediction. These capabilities are essential for the robot to accurately handle objects.
Dexterity and adaptation: The key to real-world tasks
While object detection and understanding is important, the real challenge in robotics lies in performing dexterous tasks that require fine motor skills. Even if you fold the origami fox, whether you play the game of cards or not, tasks that require high accuracy and adjustments usually exceed the capabilities of most AI systems. However, Gemini Robotics is specially designed to excel at such tasks.
- Fine athletic ability: The ability of the model to handle complex tasks such as folding, stacking objects, and playing games shows its high level of dexterity. Additional tweaks allow Gemini Robotics to handle tasks that require adjustment across multiple degrees of freedom, such as using both arms for complex operations.
- A few shot learning: Gemini Robotics also introduces the concept of small numbers of shot learning, allowing you to learn new tasks with minimal demonstrations. For example, with just 100 demonstrations, Gemini Robotics can learn to perform tasks that may otherwise require extensive training data.
- Adaptation to new embodimentsAnother important feature of Gemini Robotics is its ability to adapt to new robotic implementations. Whether it’s a bi-arm robot or a humanoid with more joints, this model can seamlessly control various types of robot bodies, making it versatile and adaptable to a variety of hardware configurations.
Zero-shot control and rapid adaptation
One of the outstanding features of Gemini Robotics is that it allows you to control your robots with zero shot or a few shot learning methods. Zero-shot control refers to the ability to perform tasks without the need for specific training for each individual task, but a small number of learning involves learning from small examples.
- Zero shot control with code generation: Gemini Robotics can generate code to control the robot even if the specific actions required have not been seen before. For example, if a high-level task description is provided, Gemini can use its inference capabilities to understand physical dynamics and environment, creating the code necessary to perform the task.
- A few shot learning: If the task requires more complex dexterity, the model can also learn from the demonstration and apply that knowledge immediately to effectively execute the task. This ability to quickly adapt to new situations is a critical advance in robotic control, especially in environments that require constant change and unpredictability.
The meaning of the future
Gemini Robotics is an important advancement in general purpose robotics. By combining AI’s reasoning capabilities with robotics and adaptability, we approach our goal of creating robots that can easily integrate into everyday life and perform a variety of tasks that require human-like interactions.
The potential applications for these models are enormous. In an industrial environment, Gemini robotics can be used for complex assembly, inspection, and maintenance tasks. At home, you can support chores, caregiving and personal entertainment. As these models continue to advance, robots could become a wide range of technologies that could open up new possibilities for multiple sectors.
Conclusion
Gemini Robotics is a suite of models built on Gemini 2.0, designed to allow robots to perform embodied inferences. These models can help engineers and developers create AI-powered robots that can understand and interact with the physical world in a human-like way. With its ability to perform complex tasks with high accuracy and flexibility, Gemini Robotics incorporates features such as embodied inference, zero shot control, and small number of shot learning. These features allow the robot to adapt to the environment without the need for extensive retraining. Gemini Robotics has the potential to transform industries, from manufacturing to home support, allowing robots to be more capable and safer in real-world applications. As these models continue to evolve, they could redefine the future of robotics.
