Zhongyu Li @ZhongyuLi4
PhD student doing robotics@UC Berkeley. Randomly post robot & cat things here. zyliatzju.github.io Joined March 2021-
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Zhongyu Li retweeted
We just did World’s first on-stage autonomous demo of long-horizon dexterous VLA 🚨 No training. No setup. Performance out of the box. Live demo is hard and unpredictable, but we felt great about our model’s generalization, and it went pretty well! 💯 Zero-shot. 100% success.
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Zhongyu Li retweeted
Meet mjlab. Powered by MuJoCo Warp. Drops Monday.
Zhongyu Li retweeted
Angry 😡
Zhongyu Li retweeted
COW
Zhongyu Li retweeted
Yesterday marked @UWaterloo's first robot learning reading group for fall 2025, and it was a great success! This week focused on robot foundation models, covering Pi0 by @physical_int and LBM by @ToyotaResearch. Shoutout to @djkesu1 for helping cohost, and @palatialXR for…
Zhongyu Li retweeted
Join us TODAY for the return of the GRASP SFI Seminar series for Fall 2025 Semester! Please welcome Tairan He who will be presenting "“Scalable Sim-to-Real Learning for General-Purpose Humanoid Skills” from 3PM-4PM. More info: grasp.upenn.edu/events/fall-20… #GRASP #GRASPLab #GRASPSFI
Amazing to see how fast the open-source humanoid (and Berkeley Humanoid Lite) community is expanding🤩!!!
Amazing to see how fast the open-source humanoid (and Berkeley Humanoid Lite) community is expanding🤩!!!
🤯🤯🤯🤯🤯
Zhongyu Li retweeted
This is just the beginning! 🌟 At @DexmateAI , we're not just building robots - we're creating intelligent partners that work alongside humans to solve real-world challenges. Proud to be part of the @NVIDIARobotics ecosystem driving this transformation.
This is just the beginning! 🌟 At @DexmateAI , we're not just building robots - we're creating intelligent partners that work alongside humans to solve real-world challenges. Proud to be part of the @NVIDIARobotics ecosystem driving this transformation.
Zhongyu Li retweeted
They say the best time to tweet about your research was 1 year ago, the second best time is now. With RAI formerly known as Boston Dynamics AI Institute, we present DiffuseCloC - the first guidable physics-based diffusion model. diffusecloc.github.io/website/
Zhongyu Li retweeted
I implemented GRPO and DPO from scratch in vanilla Pytorch to unravel every piece of training details. Hope it could be helpful for those who care about the implementation details of the algorithms. 👉 github.com/mingyin0312/RL… #AI #RL #LLM
Zhongyu Li retweeted
🔍We introduce ASearcher, a search agent trained by end2end RL Large-scale (up to 128 turns) RL with AReaL unlocks Long-Horizon Agentic Search (+20.8/+46.7% on GAIA/xBench) 💻Data, Code&Model: github.com/inclusionAI/AS… 📄Paper: arxiv.org/abs/2508.07976v #Agent #OpenSource #LLM #AGI
Zhongyu Li retweeted
July has been a big month for Viser! - Released v1.0.0😊 - We did some writing Some demos👇
Zhongyu Li retweeted
約34g、長さ22cm、羽根幅24cmのハチ型ロボット youtu.be/UzRf9y9EWqM 超広帯域技術(UWB)を使用した屋内位置特定システムにより、群れをなして自律飛行することができる #robot #robotics #Biorobotics #biomimicry #バイオミミクリー #生物模倣 #ハチロボット #BionicBee #Festo
Zhongyu Li retweeted
We interact with dogs through touch -- a simple pat can communicate trust or instruction. Shouldn't interacting with robot dogs be as intuitive? Most commercial robots lack tactile skins. We present UniTac: a method to sense touch using only existing joint sensors! [1/5]
Zhongyu Li retweeted
They are the same picture
Zhongyu Li retweeted
3D print tactile sensors anywhere inside your fin-ray fingers! We present FORTE - a solution to sensorize compliant fingers from inside with high resolution force and slip sensing. 🌐merge-lab.github.io/FORTE/ With precise and responsive tactile feedback, FORTE can gently handle…
Zhongyu Li retweeted
Time for blog post number eight -- "Vacuum grippers versus robot hands." Here's the question: when humanoids can do everything a human can, will they take over the warehouse? No! Vacuum is awesome! Vacuum grippers are the wheels of manipulation! It is only an five-minute…
Zhongyu Li retweeted
🚀 Introducing LeVERB, the first 𝗹𝗮𝘁𝗲𝗻𝘁 𝘄𝗵𝗼𝗹𝗲-𝗯𝗼𝗱𝘆 𝗵𝘂𝗺𝗮𝗻𝗼𝗶𝗱 𝗩𝗟𝗔 (upper- & lower-body), trained on sim data and zero-shot deployed. Addressing interactive tasks: navigation, sitting, locomotion with verbal instruction. 🧵 ember-lab-berkeley.github.io/LeVERB-Website/
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