Gemini Robotics On-Device brings AI to local robotic devices
DeepMind is introducing Gemini Robotics On-Device, an efficient robotics model designed to run locally on robotic hardware. The model targets general-purpose dexterity and fast task adaptation without requiring cloud inference. This represents a push toward edge deployment of frontier-scale robotics AI, reducing latency and connectivity dependencies for physical AI systems.
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Related events (8)
Gemini Robotics brings AI into the physical world
Google DeepMind has announced Gemini Robotics and Gemini Robotics-ER, two AI models purpose-built for robotic systems to perceive, reason about, and act within physical environments. The release extends the Gemini model family into embodied AI and robotics applications. Gemini Robotics-ER appears to target enhanced reasoning capabilities for robotic control. This marks a significant step by DeepMind toward deploying frontier multimodal models in physical-world settings.
Gemini Robotics 1.5 brings AI agents into the physical world
DeepMind has announced Gemini Robotics 1.5, a model designed to enable physical AI agents with capabilities spanning perception, planning, reasoning, tool use, and multi-step task execution. The release positions Gemini as a foundation for embodied robotics systems. This represents an extension of the Gemini model family into physical-world agentic applications.
Gemini Robotics-ER 1.6: Enhanced Embodied Reasoning for Autonomous Robotics
DeepMind has released Gemini Robotics-ER 1.6, an updated embodied reasoning model targeting spatial reasoning and multi-view understanding for autonomous robotics applications. The release represents an incremental update to the Gemini Robotics-ER line, focused on improving real-world task performance. The announcement comes from DeepMind's official blog, indicating a production-grade capability update rather than a research preview.
Gemini 3.5: Frontier Intelligence with Action
Google DeepMind has announced Gemini 3.5, a new model generation positioned around agentic capabilities and complex workflow execution. The announcement emphasizes action-oriented AI, suggesting a focus on tool use, multi-step reasoning, and autonomous task completion. The blog post is brief, indicating this may be an initial announcement with further details to follow.
Introducing the Gemini 2.5 Computer Use model
Google DeepMind has released a preview of a specialized Computer Use model built on Gemini 2.5 Pro, available via API. The model is designed to power agents that can interact with user interfaces, extending Gemini 2.5 Pro's capabilities into computer-use agentic tasks. This positions Google as a direct competitor to Anthropic's Claude Computer Use and similar offerings in the emerging computer-use agent space.
Gemini 2.5: Google DeepMind's Most Intelligent AI Model with Built-in Thinking
Google DeepMind has announced Gemini 2.5, described as their most intelligent AI model to date, with thinking capabilities built directly into the model. The announcement comes from the official DeepMind blog and marks a significant step in Google's frontier model development. The integration of thinking natively into the model suggests a chain-of-thought or reasoning-first architecture similar to approaches seen in competing models.
Reachy Mini goes fully local
A Hugging Face blog post describes running the Reachy Mini robot's conversational AI stack entirely on local hardware, eliminating cloud dependencies. The post likely covers the models, tooling, and inference setup required to achieve on-device operation for a small consumer robot. This represents a deployment case study at the intersection of edge inference and robotics.
Gemini 3.1 Flash-Lite: Built for intelligence at scale
Google DeepMind has released Gemini 3.1 Flash-Lite, described as the fastest and most cost-efficient model in the Gemini 3 series. The announcement positions it as optimized for high-throughput, cost-sensitive deployments at scale. The body is sparse, offering no benchmark details or capability specifics beyond the efficiency framing.