🚀 Introducing LLaVA-o1: The first visual language model capable of spontaneous, systematic reasoning, similar to GPT-o1! 🔍 🎯Our 11B model outperforms Gemini-1.5-pro, GPT-4o-mini, and Llama-3.2-90B-Vision-Instruct! 🔑The key is training on structured data and a novel inference

🍌We present DenseMatcher！ 🤖️DenseMatcher enables robots to acquire generalizable skills across diverse object categories by only seeing one demo, by finding correspondences between 3D objects even with different types, shapes, and appearances.

When I was a Ph.D. student at Caltech, Ludwig Schmidt discussed the paper "Do ImageNet Classifiers Generalize to ImageNet?" in his job talk, which left me with a super deep impression until today. Basically, they recreated an ImageNet and found the SOTA models in circa 2019 had

𝑫𝒆𝒎𝒐𝑮𝒆𝒏 has been accepted to #RSS2025 🥳 See you in LA this June 🙌

Excited to share that MENTOR has been accepted to #ICML2025! See you in Vancouver this July🤖 x.com/suning_huang/s…

🧑🤖 Introducing Human2Sim2Robot!  💪🦾 Learn robust dexterous manipulation policies from just one human RGB-D video. Our Real→Sim→Real framework crosses the human-robot embodiment gap using RL in simulation. #Robotics #DexterousManipulation #Sim2Real 🧵1/7

Introducing Mobi-π: Mobilizing Your Robot Learning Policy. Our method: ✈️ enables flexible mobile skill chaining 🪶 without requiring additional policy training data 🏠 while scaling to unseen scenes 🧵↓

How can we move beyond static-arm lab setups and learn robot policies in our messy homes? We introduce HoMeR, an imitation learning agent for in-the-wild mobile manipulation. 🧵1/8

What makes data “good” for robot learning? We argue: it’s the data that drives closed-loop policy success! Introducing CUPID 💘, a method that curates demonstrations not by "quality" or appearance, but by how they influence policy behavior, using influence functions. (1/6)

Now in Nature! 🚀 Our method learns a controllable 3D model of any robot from vision, enabling single-camera closed-loop control at test time! This includes robots previously uncontrollable, soft, and bio-inspired, potentially lowering the barrier of entry to automation! Paper:

Unfortunately I cannot attend the conference in person this year, but our co-author Guowei Xu will be presenting the paper and answer all your questions! 📜Poster session: Time: Wed 16 Jul 11 a.m. PDT — 1:30 p.m. PDT Location: West Exhibition Hall B2-B3 #W-607

𝗜'𝘃𝗲 𝗵𝗲𝗮𝗿𝗱 𝘁𝗵𝗶𝘀 𝗮 𝗹𝗼𝘁 𝗿𝗲𝗰𝗲𝗻𝘁𝗹𝘆: "𝗪𝗲 𝘁𝗿𝗮𝗶𝗻𝗲𝗱 𝗼𝘂𝗿 𝗿𝗼𝗯𝗼𝘁 𝗼𝗻 𝗼𝗻𝗲 𝗼𝗯𝗷𝗲𝗰𝘁 𝗮𝗻𝗱 𝗶𝘁 𝗴𝗲𝗻𝗲𝗿𝗮𝗹𝗶𝘀𝗲𝗱 𝘁𝗼 𝗮 𝗻𝗼𝘃𝗲𝗹 𝗼𝗯𝗷𝗲𝗰𝘁 - 𝘁𝗵𝗲𝘀𝗲 𝗻𝗲𝘄 𝗩𝗟𝗔 𝗺𝗼𝗱𝗲𝗹𝘀 𝗮𝗿𝗲 𝗰𝗿𝗮𝘇𝘆!" Let's talk about what's actually

Introducing Masquerade 🎭: We edit in-the-wild videos to look like robot demos, and find that co-training policies with this data achieves much stronger performance in new environments. ❗Note: No real robots in these videos❗It’s all 💪🏼 ➡️ 🦾 🧵1/6