Yufeng Tian

Incoming PhD of MMLab@CUHK.

[Sizhe Yang @SizheYang111]

💡Can a WAM remember long-horizon history while staying efficient?

🔥 Introducing MemoryWAM: Efficient World Action Modeling with Persistent Memory

🔗 https://t.co/Ao9I4OHtY2

[Zhecheng Yuan @fancy_yzc]

🌶️🤖𝐖𝐞 𝐜𝐨𝐨𝐤𝐞𝐝 𝐭𝐡𝐞 𝐫𝐨𝐛𝐨𝐭𝐢𝐜𝐬 𝐰𝐨𝐫𝐥𝐝’𝐬 𝐟𝐢𝐫𝐬𝐭 𝐩𝐥𝐚𝐭𝐞 𝐨𝐟 𝐌𝐚𝐩𝐨 𝐓𝐨𝐟𝐮.🤖🌶️

This was not just a cooking demo. It was a 30-minute, long-horizon robotics challenge packed with delicate, continuous, high-dexterity actions.

With only a small number of demonstrations, our model learned to perform complex manipulation over an extended sequence. At the same time, we pushed hard on motion control and system-level optimization, making the robot move smoothly while keeping the high success rate.

For us, this plate of Mapo Tofu is more than a dish. It is a small but meaningful step toward home robots that can handle real everyday tasks with the dexterity, consistency, and reliability people expect.

[Yufeng Tian @TianRainWind]

@manutd_moon Fantastic work! Congrats, Dongjie!

[Yufeng Tian @TianRainWind]

@ChiChu299792458 Amazing work. Congrats Chi!

[Yufeng Tian @TianRainWind]

@ChiChu299792458 Thanks, Chi!

[Yufeng Tian @TianRainWind]

Eyes see. Hands feel. Many visuotactile methods rely only on concatenation, leaving room to better exploit alignment and complementarity for manipulation.
Excited to share our #ICRA2026 paper: "ViTaS: Visual Tactile Soft Fusion Contrastive Learning for Visuomotor Learning".

[Yufeng Tian @TianRainWind]

@shuiqi28 @HarryXu12 @fancy_yzc @still_wtm @Will_Zhou2025 @Yuanhang__Zhang @ZixianLiu03 6/N
Paper and code have been released. Check them out!
Paper: https://t.co/s9NsoJf6dw
Code: https://t.co/x44cttiYhN

[Yufeng Tian @TianRainWind]

5/N
ViTaS is co-led by Shuiqi Cheng @shuiqi28, advised by Huazhe Xu @HarryXu12 and Zhecheng Yuan @fancy_yzc. Also shoutout to Tianming Wei @still_wtm , Tianxing Zhou @Will_Zhou2025, Yuanhang Zhang @Yuanhang__Zhang, Zixian Liu @ZixianLiu03 and Qianwei Han for their great effort!

[Yufeng Tian @TianRainWind]

Results: For RL, ViTaS shows significant improvement in 12 tasks. For IL, ViTaS still wins across 3 simulation IL tasks, showing that touch compensates for reduced vision.

[Yufeng Tian @TianRainWind]

2/N ViTaS in RL and IL.
In RL we use ViTaS as the feature extractor with PPO. In IL we plug ViTaS into DP, replacing its default encoders with our visuo-tactile pipeline.
Fair comparison: DP gets 3 cameras, while ViTaS uses only 1 head camera + tactile sensors.

[Kun Lei @kunlei15]

Real-world deployment is crucial for robotics.

I wrote about a recipe: strong base model + RL post-training (offline & online) → toward human-level capability.
https://t.co/DGlxVjSduH

[Yufeng Tian @TianRainWind]

Achieving 100% success rate is really brilliant!

[Kun Lei @kunlei15]

Introducing RL-100: Performant Robotic Manipulation with Real-World Reinforcement Learning. https://t.co/FUqEQ26mzi

7 real robot tasks, 900/900 successes. Up to 250 consecutive trials in one task, running 2 hours nonstop without failure.

High success rate against physical disturbances, zero-shot, and few-shot adaptation

Our first step toward a deployable robot learning system.

[Yufeng Tian @TianRainWind]

@4nsllotk One of the best galgames I ever played. GL & HF!