Wenhao Yu

Google DeepMind 🤝 @BostonDynamics Our new research partnership will bring together our advancements in Gemini Robotics’s foundational capabilities to their new Atlas® humanoids. 🦾 Find out more → goo.gle/49paguA

We’re making robots more capable than ever in the physical world. 🤖 Gemini Robotics 1.5 is a levelled up agentic system that can reason better, plan ahead, use digital tools such as @Google Search, interact with humans and much more. Here’s how it works 🧵

🤖🌎 We are organizing a workshop on Robotics World Modeling at @corl_conf 2025! We have an excellent group of speakers and panelists, and are inviting you to submit your papers with a July 13 deadline. Website: robot-world-modeling.github.io

How can we connect world models to physical world? Come join our 2025 workshop at ICML on Building Physically Plausible World Models! physical-world-modeling.github.io (1/2)

Meet Gemini Robotics: our latest AI models designed for a new generation of helpful robots. 🤖 Based on Gemini 2.0, they bring capabilities such as better reasoning, interactivity, dexterity and generalization into the physical world. 🧵 goo.gle/gemini2-roboti…

Announcing the 2nd Earth Rover Challenge: an "AI vs Gamers" global navigation competition (to be held #ICRA2025 in May in Atlanta) Co-organized with researchers from Deepmind, Meta & academia A thread 🧵 - 1/n

Welcome to the world, Gemini 2.0 ✨ our most capable AI model yet. We're first releasing an experimental version of 2.0 Flash ⚡ It has better performance, new multimodal output, @Google tool use - and paves the way for new agentic experiences. 🧵 goo.gle/gemini-2

Excited to finally share Generative Value Learning (GVL), my @GoogleDeepMind project on extracting universal value functions from long-context VLMs via in-context learning! We discovered a simple method to generate zero-shot and few-shot values for 300+ robot tasks and 50+ datasets using SOTA VLMs like Gemini (Try out the demo on our website on your robot video today!) I worked a lot on leveraging foundation models as guidance for robots in my PhD, and to me, this result forges a new frontier in how we can use foundation models for robot learning, given its broad applicability independent of embodiment and task types. Quite excited about how we can build on this work as a community!

LocoMan = Quadrupedal Robot + 2 * Loco-Manipulator Powered by dual lightweight 3-DoF Loco-Manipulators and the Whole-Body Controller, LocoMan achieves various challenging tasks, such as manipulation in narrow spaces and bimanual-manipulation. linchangyi1.github.io/LocoMan 👇👇👇