Google DeepMind has introduced Gemini Robotics, an advanced AI model based on Gemini 2.0, designed to bring artificial intelligence into the physical world. Unlike previous models confined to digital tasks, Gemini Robotics incorporates vision, language, and action capabilities, allowing robots to interact with their surroundings and perform complex real-world tasks.
The model excels in three key areas: generalization, interactivity, and dexterity. It adapts to novel environments, understands diverse instructions, and responds dynamically to changes. This adaptability makes it capable of handling tasks it has never encountered before.
Additionally, its intuitive interaction allows it to process natural language commands, monitor surroundings, and adjust its actions accordingly. The model’s dexterity enables precise object manipulation, making it suitable for tasks requiring fine motor skills.
Alongside Gemini Robotics, DeepMind introduced Gemini Robotics-ER, a model with enhanced spatial reasoning. It allows roboticists to integrate AI with their own programs, improving real-world applications like grasping objects and path planning.
This embodied reasoning capability significantly boosts robot efficiency, with the model achieving up to three times higher success rates in end-to-end control compared to Gemini 2.0.
To advance AI safety in robotics, DeepMind is implementing layered security measures. Gemini Robotics-ER integrates safety controllers to ensure robots operate within predefined limits, avoiding collisions and unsafe actions.
Additionally, DeepMind is developing a dataset to refine AI safety protocols and align robotic behaviors with human values.
DeepMind is collaborating with partners, including Apptronik, to develop humanoid robots using Gemini 2.0. Other industry leaders such as Boston Dynamics and Agility Robotics are also testing Gemini Robotics-ER. With these advancements, DeepMind aims to revolutionize AI-driven robotics, pushing toward a future where robots seamlessly assist in homes, workplaces, and beyond.
