Yixin Chen

Research Scientist at BIGAI, prev @UCLA, @MPI_IS, @Amazon

[Yixin Chen @_yixinchen]

@NanXue7 Impressive results! Congrats!

[Yixin Chen @_yixinchen]

Co-organizing with: @sfwang0928 , @BaoxiongJ , @zouchuhang , @AnpeiC , Yixin Zhu, @chenfanfujiang , @SiyuTang3 , @siyuanhuang95 , Andreas Geiger, Derek Hoiem, Song-Chun Zhu

[Yixin Chen @_yixinchen]

Join us in the heart of the Rockies @CVPR . 🔥🔥

The 6th workshop on 3D scene understanding for vision, graphics, and robotics #3DSUN is around the corner, and we have a superb lineup of speakers from both top-tier academia and leading industry.

Mark: June 4th, starting from 8:15 AM in Room 610/612! #CVPR2026
https://t.co/EMyCRduRTt

[Yixin Chen @_yixinchen]

Speakers: Andrew Davison @AjdDavison , Andrea Vedaldi, Ziwei Liu @liuziwei7 , Gerard Pons-Moll @GerardPonsMoll1 , Lingjie Liu @LingjieLiu1 , Songyou Peng @songyoupeng

[Ihtesham Ali @ihtesham2005]

A mathematician who shared an office with Claude Shannon at Bell Labs gave one lecture in 1986 that explains why some people win Nobel Prizes and other equally smart people spend their whole lives doing forgettable work.

His name was Richard Hamming. He won the Turing Award. He invented error-correcting codes that made modern computing possible. And he spent 30 years at Bell Labs sitting in a cafeteria at lunch watching which scientists became legendary and which ones faded into nothing.

In March 1986, he walked into a Bellcore auditorium in front of 200 researchers and told them exactly what he had seen.

Here's the framework that has been quoted by every serious scientist for the last 40 years.

His opening line landed like a punch. He said most scientists he worked with at Bell Labs were just as smart as the Nobel Prize winners. Just as hardworking. Just as credentialed. And yet at the end of a 40-year career, one group had changed entire fields and the other group was forgotten by the time they retired.

He wanted to know what the difference actually was. And he said it wasn't luck. It wasn't IQ. It was a specific set of habits that almost nobody is willing to follow.

The first habit was the one that hurts the most to hear. He said most scientists deliberately avoid the most important problem in their field because the odds of failure are too high. They pick a safe adjacent problem, solve it cleanly, publish it, and move on. And because they never swing at the hard problem, they never hit it. He said if you do not work on an important problem, it is unlikely you will do important work. That is not a motivational line. That is a logical one.

The second habit was about doors. Literal doors. He noticed that the scientists at Bell Labs who kept their office doors closed got more done in the short term because they had no interruptions. But the scientists who kept their doors open got more done over a career. The open-door scientists were interrupted constantly. They also absorbed every new idea passing through the hallway. Ten years in, they were working on problems the closed-door scientists did not even know existed.

The third habit was inversion. When Bell Labs refused to give him the team of programmers he wanted, Hamming sat with the rejection for weeks. Then he flipped the question. Instead of asking for programmers to write the programs, he asked why machines could not write the programs themselves. That single inversion pushed him into the frontier of computer science. He said the pattern repeats everywhere. What looks like a defect, if you flip it correctly, becomes the exact thing that pushes you ahead of everyone else.

The fourth habit was the one that hit me the hardest. He said knowledge and productivity compound like interest. Someone who works 10 percent harder than you does not produce 10 percent more over a career. They produce twice as much. The gap doesn't add. It multiplies. And it compounds silently for years before anyone notices.

He finished the lecture with a line I have never been able to shake.

He said Pasteur's famous quote is right. Luck favors the prepared mind. But he meant it literally. You don't hope for luck. You engineer the conditions where luck can land on you. Open doors. Important problems. Inverted questions. Compounded hours. Those are not traits. Those are choices you make every single day.

The transcript has been sitting on the University of Virginia's computer science website for almost 30 years. The video is free on YouTube. Stripe Press reprinted the full lectures as a book in 2020 and Bret Victor wrote the foreword.

Hamming died in 1998. He gave his final lecture a few weeks before. He was 82.

The lecture that explains why some careers become legendary and others disappear is still free. Most people who could benefit from it will never open it.

[Hangxin Liu @HangxinLiu]

Check out our new paper: Cross-Robot Behavior Adaptation through Intention Alignment

We enable skill generalization across different embodiments and tasks by encoding both intention descriptions and robot motion generators into a shared latent space.

[Yixin Chen @_yixinchen]

@Michael_J_Black Congrats, Michael, and the impact continues!

[Siyuan Huang @siyuanhuang95]

You might have seen the WuBOT performing at the 2026 Spring Festival Gala; however, most high-dynamic extreme motions you see are executed by overfitted tracking policies. Until now, training a unified policy capable of performing various extreme motions with a high success rate remained an unsolved challenge.

We spent an entire year digging into the barrier between general tracking and extreme physical behaviors. After burning through dozens of G1 robots, we finally identified the bottleneck of learning and physical executability.

With these discoveries, we developed OmniXtreme: the first general policy that can execute diverse extreme motions, including consecutive flips, extreme balancing, and even breakdancing with rapid contact switches!

This capability is achieved by pre-training a flow-based generative control policy and then post-training with actuation-aware residual RL for complex physical dynamics—a step we found critical for successful real-world transfer.

This work is a joint collaboration with @UnitreeRobotics. Together, we are pushing the physical limits of humanoid robots. It is incredibly exciting to see a general "robot gymnast" and "robot breakdancer" come to life! It was also our first time publishing a paper with XingXing, which was an enlightening experience.

The model checkpoints are now released—we welcome you to play with them! 📦

📄 Paper: https://t.co/ySKLac8w6i
🌐 Project: https://t.co/ortlLW24JB
💻 Code: https://t.co/IJ4XHYT4Qj

[Yixin Chen @_yixinchen]

@Michael_J_Black Congrats! So well deserved!

[Yixin Chen @_yixinchen]

@sha2nk_t @Michael_J_Black Congrats!

[RoboPapers @RoboPapers]

Robots need to be able to apply pressure and make contact with objects as needed in order to accomplish their tasks. From compliance to working safely around humans to whole-body manipulation of heavy objects, combining force and position control can dramatically expand the capabilities of robots. This is especially true for legged robots, which have so much ability to exert forces on the world around them. But how do we train robots which can do this?

@BaoxiongJ tells us more in our discussion of his team’s recent, Best Paper Award winning work on learning a unified policy for position and force control, called UniFP.

To learn more, watch Episode #49 of RoboPapers, hosted by @micoolcho and @chris_j_paxton.

[Dmytro Mishkin 🇺🇦 @ducha_aiki]

Colmap-vs-VGGT, reposting for better discussion visibility

[Ashok Elluswamy @aelluswamy]

https://t.co/Jr4i7tHUCr

[Yixin Chen @_yixinchen]

@liangqiu_1994 GL, ndlg.

[Yixin Chen @_yixinchen]

#CVPR2025 is just around the corner!🔥🔥

Join us for the exciting roster of distinguished speakers at the 5th Workshop on 3D Scene Understanding for Vision, Graphics, and Robotics. #3DSUN

Mark: June 11th, starting from 8:45 AM in Room 106C! @CVPR

https://t.co/rfxNU8ipRW

[Peyman Milanfar @docmilanfar]

You should be so lucky to have people throughout your research career that you can openly bounce ideas to and from - especially if they complement your strengths in your areas of weakness - it is a rare and precious gift.

[Yixin Chen @_yixinchen]

@jon_barron Wow, thanks for sharing, and it really helps to clear minds about 2D and 3D. Such an enjoyment to watch, and it hypes when the cost vs num_frames line points to infinity.

[Yixin Chen @_yixinchen]

Taccel for tactile simulation, for robots that touch, feel, and move!🤖🤖🤖

[Siyuan Huang @siyuanhuang95]

🤖🤖🤖 Following RoboVerse, we introduce another work focused on Robotic Tactile Simulation - Taccel Simulator. Taccel is a high-performance simulation platform for vision-based tactile sensors and robots.

🚀🚀🚀 Boosted by Nvidia Warp, we optimize Taccel with highly parallelized simulations and support 900fps simulation with 4k+ parallel training envs.

🤝🤝🤝 Taccel is designed with user-friendly APIs and is easy to use. We open-sourced all the code and documentation. Feel free to try!

Project: https://t.co/AT0G7MGzqX
Preprint: https://t.co/wSMUqBCwQB
Code: https://t.co/H5CxVjg5Ke

[Hangxin Liu @HangxinLiu]

5 km run, zero mistakes. 🏃‍♂️🤖 Warming up for the Beijing Robot Half Marathon. #RobotMarathon

[Yixin Chen @_yixinchen]

🔥AI + vision + robots!🔥
Join the conversation at our 5th Workshop on 3D Scene Understanding for Vision, Graphics, and Robotics at #CVPR2025.

🔗 Learn more: https://t.co/QI09Deroku

[#CVPR2026 @CVPR]

#AI-enhanced #robotics has become a growth area in #computervision, and the #CVPR2025 program reflects it. According to the technical program chairs, it’s an emerging trend for the conference.

Article: https://t.co/jBbbqYsUR3