Gemini Robotics On-Device brings AI to local robotic devices

Gemini Robotics 1.5 brings AI agents into the physical world

We’re powering an era of physical agents — enabling robots to perceive, plan, think, use tools and act to better solve complex, multi-step tasks.

Gemini Robotics 2 brings whole body intelligence to robots

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

Gemini Robotics ER 1. 6: Enhancing spatial reasoning and multi-view understanding for autonomous robotics.

Bringing Robotics AI to Embedded Platforms: Dataset Recording, VLA Fine‑Tuning, and On‑Device Optimizations

Our vision for building a universal AI assistant

We’re extending Gemini to become a world model that can make plans and imagine new experiences by simulating aspects of the world.

Gemini Robotics ER 2: powering robotics with video understanding, task orchestration, and multi-robot collaboration

Gemini Robotics ER 2 helps robots reason, collaborate, and solve real-world tasks. It represents a step change in video understanding, tool orchestration, and multi-robot collaboration for robotic applications.

Gemini for Science: AI experiments and tools for a new era of discovery

A collection of science tools and experiments to expand the scale and precision of scientific exploration.

Learning dexterity

We’ve trained a human-like robot hand to manipulate physical objects with unprecedented dexterity.

Principles of Robot Autonomy

arXiv:2608. 03496v1 Announce Type: cross Abstract: Autonomous robots are moving rapidly from research labs into everyday life - on roads, in the air, in warehouses, and in space.

Reachy Mini - The Open-Source Robot for Today's and Tomorrow's AI Builders

ORCA: A Platform for Open-Source Dexterity Research

arXiv:2606. 14561v1 Announce Type: cross Abstract: Robotics manipulation research increasingly focuses on two-finger parallel grippers for their effectiveness, affordability, and ease of teleoperation.

CLIFT: Turning Gemini Robotics On-Device into Humanoid Specialists via Non-Invasive Closed-Loop Iterative Fine-Tuning

arXiv:2607. 29172v1 Announce Type: cross Abstract: While robot foundation models are growing increasingly capable, the strongest models are typically trained on proprietary data and remain closed-source, limiting downstream users' ability to adapt them to new tasks, embodiments, and deployment settings.