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Control a robot in simulation with the Gemini Robotics Embodied Reasoning model 🤖 Direct tasks like object localization and placement by simply prompting a robotic arm to pick up and place objects.

Google AI Developers

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1,296,404 görüntüleme • 4 ay önce •via X (Twitter)

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We teamed up with Boston Dynamics to power their robot Spot with Gemini Robotics embodied reasoning models. This means it can better understand its surroundings, identify objects and follow simple commands - like tidying up a room.

We teamed up with Boston Dynamics to power their robot Spot with Gemini Robotics embodied reasoning models. This means it can better understand its surroundings, identify objects and follow simple commands - like tidying up a room.

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One more thing 🚀 Qwen’s agentic capability is no longer limited to the digital world — we’re bringing it into physical world. With our in-house robotic agentic system and navigation model, Qwen can now control a robot to execute tasks in real-world. #qwen #embodied #robotics

One more thing 🚀 Qwen’s agentic capability is no longer limited to the digital world — we’re bringing it into physical world. With our in-house robotic agentic system and navigation model, Qwen can now control a robot to execute tasks in real-world. #qwen #embodied #robotics

xiong-hui (barry) chen

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Google DeepMind introduced two foundational models for embodied reasoning, enabling robots to comprehend, react, and take action in the physical world: ⦿ Gemini Robotics – built on Gemini 2.0. Integrates vision, language, and action for real-world dexterity, . ⦿ Gemini Robotics-ER – Enhances spatial reasoning for advanced robotic control. They are working with Apptronik to develop the next generation of humanoid robots.

Google DeepMind introduced two foundational models for embodied reasoning, enabling robots to comprehend, react, and take action in the physical world: ⦿ Gemini Robotics – built on Gemini 2.0. Integrates vision, language, and action for real-world dexterity, . ⦿ Gemini Robotics-ER – Enhances spatial reasoning for advanced robotic control. They are working with Apptronik to develop the next generation of humanoid robots.

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Google DeepMind introduces Gemini Robotics 1.5, enabling robots to perceive, plan, think, use tools, and act on complex tasks. The agentic framework comprises: ⦿ Gemini Robotics-ER 1.5 (VLM): Orchestrates high-level embodied reasoning and planning. ⦿ Gemini Robotics 1.5 (VLA): Converts visuals and instructions provided by ER 1.5 into actions.

Google DeepMind introduces Gemini Robotics 1.5, enabling robots to perceive, plan, think, use tools, and act on complex tasks. The agentic framework comprises: ⦿ Gemini Robotics-ER 1.5 (VLM): Orchestrates high-level embodied reasoning and planning. ⦿ Gemini Robotics 1.5 (VLA): Converts visuals and instructions provided by ER 1.5 into actions.

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For an industrial robot built for the rigors of factories and power plants, tidying up a living room may seem like a light day at the office for Spot. Yet, this demonstration represents the promise of AI models in robotics. In this case, Google DeepMind's visual-language model (VLM) Gemini Robotics-ER 1.5 was empowering Spot with embodied reasoning. Go behind the scenes and watch the full demo in our blog:

For an industrial robot built for the rigors of factories and power plants, tidying up a living room may seem like a light day at the office for Spot. Yet, this demonstration represents the promise of AI models in robotics. In this case, Google DeepMind's visual-language model (VLM) Gemini Robotics-ER 1.5 was empowering Spot with embodied reasoning. Go behind the scenes and watch the full demo in our blog:

Boston Dynamics

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THIS IS VIBE CODING ROBOTICS. Someone just built a complete robot arm simulation using Gemini 3. AI wrote the inverse kinematics from scratch the robot stacks cubes to build walls.

THIS IS VIBE CODING ROBOTICS. Someone just built a complete robot arm simulation using Gemini 3. AI wrote the inverse kinematics from scratch the robot stacks cubes to build walls.

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A paper in Nature Communications describes a detachable robotic hand that can crawl and grab objects. The design enables tasks such as retrieving objects beyond normal reach and performing multi-object handling.

A paper in Nature Communications describes a detachable robotic hand that can crawl and grab objects. The design enables tasks such as retrieving objects beyond normal reach and performing multi-object handling.

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Robot vacuum and mop with a robotic arm that picks up trash 😲

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Stop prompting for "a room" and start prompting for "a mood." Create visual tension in your scenes with subject placement, lighting, and objects.

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Google Flow

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Very Powerful but very simple Prompting technique. Simply ask the LLM to re-read the question - and this significantly boosts LLM reasoning across diverse tasks and model types. 💡 Repeats question input twice in prompt, unlocks latent reasoning potential **Original Problem** 🤔: Decoder-only LLMs with unidirectional attention struggle with nuanced reasoning tasks due to limited global understanding of input questions. **Key Insights from this Paper **💡: • Re-reading (RE2) input enhances reasoning by improving question comprehension • Enables "bidirectional" understanding in unidirectional LLMs • Compatible with existing thought-eliciting prompting methods • Effective across various LLM types and reasoning tasks

Very Powerful but very simple Prompting technique. Simply ask the LLM to re-read the question - and this significantly boosts LLM reasoning across diverse tasks and model types. 💡 Repeats question input twice in prompt, unlocks latent reasoning potential Original Problem 🤔: Decoder-only LLMs with unidirectional attention struggle with nuanced reasoning tasks due to limited global understanding of input questions. Key Insights from this Paper 💡: • Re-reading (RE2) input enhances reasoning by improving question comprehension • Enables "bidirectional" understanding in unidirectional LLMs • Compatible with existing thought-eliciting prompting methods • Effective across various LLM types and reasoning tasks

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How to rotate a tomato with a potato using a robot hand? 🤖🍅🥔 Our new model, Robot Synesthesia, blends touch and vision to manipulate multiple objects, even non-convex ones like a cross!

How to rotate a tomato with a potato using a robot hand? 🤖🍅🥔 Our new model, Robot Synesthesia, blends touch and vision to manipulate multiple objects, even non-convex ones like a cross!

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What happens when we train the largest vision-language model and add in robot experiences? The result is PaLM-E 🌴🤖, a 562-billion parameter, general-purpose, embodied visual-language generalist - across robotics, vision, and language. Website:

What happens when we train the largest vision-language model and add in robot experiences? The result is PaLM-E 🌴🤖, a 562-billion parameter, general-purpose, embodied visual-language generalist - across robotics, vision, and language. Website:

Danny Driess

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Qwen-VLA feels like one of the first real robotics foundation models. A single system trained across robot manipulation, navigation, egocentric human video, simulation, and vision-language reasoning instead of isolated robot policies.

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📢 First contact between a frontier model and robots! Gemini Robotics is a SOTA generalist Vision-Language-Action model bringing frontier model intelligence to the physical world. It's an extremely capable model enabling dexterous, steerable, and general robot control. 🧵⬇️

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Watch our HT-Panthera six-axis master-slave robotic arm in action—from playing ping pong to precise object grasping.🤖✨ #HighTorqueRobotics #HTPanthera #EmbodiedAI #Robotics #RoboticArm

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It’s been a massive week for embodied AI foundation models: the pace of this field is truly staggering. Throwing it back to the SimToolReal work by Kushal and Tyler Lum (Cornell & Stanford labs). In February, they achieved zero-shot tool manipulation across 24 tasks using a single RL policy and a robotic arm fitted with the SharpaWave hand. Watching the robot nail these high-speed in-hand rotations is incredible. The precision is especially impressive when you consider the policy was never trained on these specific objects or tasks. This is what solving the manipulation bottleneck looks like. ⚡️

It’s been a massive week for embodied AI foundation models: the pace of this field is truly staggering. Throwing it back to the SimToolReal work by Kushal and Tyler Lum (Cornell & Stanford labs). In February, they achieved zero-shot tool manipulation across 24 tasks using a single RL policy and a robotic arm fitted with the SharpaWave hand. Watching the robot nail these high-speed in-hand rotations is incredible. The precision is especially impressive when you consider the policy was never trained on these specific objects or tasks. This is what solving the manipulation bottleneck looks like. ⚡️

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