Yu Lei

PhD student @UTCompSci | Learn to understand ourselves and build intelligence.🤖🧠👁️

[Yu Lei @_OutofMemory_]

🤖Co-training is everywhere (sim↔real[e.g. GR00T, LBM], human↔robot[e.g. PI, EgoScale], even non-robot data[e.g. PI, LBM).
But why does it work? How can we improve it further?

Taking sim-and-real imitation learning in diffusion/ flow-based models as the test bed, we performed a rigorous mechanistic analysis, drawing on theoretical insights and multi-layered experiments.

😮Key insight: it’s all about representations.
- Alignment → enables transfer
- Discernibility → enables adaptation

⚖️Both are necessary — it's better to have more aligned representations, but the model must be able to discern the domains. We term this as structured representation alignment.

⬇️Let’s take a deep dive into that:
Paper: https://t.co/RWCAxdBC0j
Website: https://t.co/BwgbwCkevA

[Yu Lei @_OutofMemory_]

@yuxuank02 Yess

[Yu Lei @_OutofMemory_]

It is a spiral upwards.

Millions of yrs precipitation -> the data/data infrastructure we have now.

As we have massive labs trying to maximize the strength, it’s beneficial to keep thinking about sample efficiency at the same time.

[Nathan Lambert @natolambert]

I feel like the obsession with continual learning / sample efficiency leads the field in the wrong direction. It's the bad career strategy of focusing on addressing your weaknesses instead of maximizing your strengths.

Yes, there is an existence proof in the human brain, but it doesn't by any means guarantee that that'll be the most interesting AI. It may require $100T of R&D on chips and AI methods to get that unlock.

On the other side of things, it's obvious that the coming models are extremely transformative and built on technologies that we already have. There's great reason to focus on just maximizing this. In reality, this is what the frontier labs are doing. They're going as fast as possible down the current development tree. This is good for progress and mixed for safety/geopolitics.

Things like "automate white color work" and "replace the AI researcher job" are the guesses of labs because it's super hard to imagine futures for what these dramatic technologies will be. Don't take the labs too seriously about this being the exact goal. The exact goal is to push the frontier and monetize later.

Solving continual learning, sample efficiency, etc would be great, but its trying to predict when a scientific breakthrough will come instead of trying to grapple with how the 100% sure thing coming technological revolution will change our lives.

This isn't to say the Dwarkesh post is bad, it addresses some reasonable critiques, but it is the least bitter lesson pilled thing to be obsessed with human intelligence and how that can inform AI.

We are in the AGI era of research. This is about embracing the unknown, scaling resources, and seeing what is enabled by making a series of magical tweaks to complex recipes that build frontier models. Lean into the alchemy.

(it should be pretty clear that I personally, investing in open research agree we need fundamental science -- just not agreeing that this is what the "cutting edge of the frontier" is governed by)

[Yu Lei @_OutofMemory_]

Really cool

[Yaman＠"体験を、拡張せよ" @yaman_xr]

フィジカルAI、ついに独り立ち！

テレオペなし・自律で、もしかめ5回成功。けん玉6級レベルに到達。

そして今日は私の誕生日。39歳、だけにサンキュー。

[Yu Lei @_OutofMemory_]

@_yudaitanaka @UTCompSci Welcome Yudai!!🤠

[Rutav @rutavms]

Can we build generalist robots with zero teleoperation? Come participate in the discussion and weigh in at our ICRA'26 workshop, BeyondTeleop, starting at 8.45 am CEST today (June 5th)!

📍 Strauss 3

[Ye Yuan @_ye_yuan]

Humanoid robotics is hitting a data wall. Teleop and mocap took us far, but they don’t scale to every object, terrain, and behavior.

We’re releasing GRAIL: https://t.co/LxTKtMPtw0 — a fully digital pipeline for generating loco-manipulation data before the robot moves. 🧵(1/8)

[Yuliang Xiu @yuliangxiu]

天行有常，不以尧存不以桀亡

[Conference on Robot Learning @corl_conf]

The CoRL 2026 keynote lineup is here!
🔹 Russ Tedrake — MIT; stealth startup @RussTedrake
🔹 Fei-Fei Li — Stanford; World Labs @drfeifei
🔹 Wolfram Burgard — UT Nuremberg @wolfram_burgard

Join us in Austin this November.
https://t.co/uiOkizDNIc

[Yuke Zhu @yukez]

Exciting news on GR00T:

NVIDIA announces our first open humanoid robot platform, featuring Unitree H2 Plus and Sharpa hands, to accelerate academic research and facilitate cross-institutional collaboration.

R&D in humanoid robotics needs broader participation. Open science is how we build the future faster, together.

[Yu Lei @_OutofMemory_]

Scale your humanoid motion data with motion planning in other domains, then transfer to real!! - try out the new great work from data gen cizar👑 @linkevin0

What a line of work: DexMimicGen -> CP-Gen -> HumanoidMimicGen!

[Kevin Lin @linkevin0]

Humanoids need data. Lots and lots of data.

Introducing HumanoidMimicGen: a method that automatically generates 1000s of humanoid loco-manipulation demonstrations from a single teleoperated demonstration.

[Youngsun Wi @WiYoungsun]

Dexterous hands vary widely—so do tactile modalities. 🖐️🌈
Our vision on tactile human-to-robot transfer:
🔓 Not tied to specific hardware
♻️ Reuse human tactile demos across embodiments

Presenting TactAlign, a cross-sensor tactile alignment for cross-embodiment policy transfer.

[Demis Hassabis @demishassabis]

Gemini Omni is a major leap in world understanding & multimodal editing! It can take photos, video & audio and build entirely new scenes. Over time it’ll be able to handle any input & any output - starting w/ video

You can even give it your own videos & iterate on your ideas:

[Yuke Zhu @yukez]

I will be in Vienna in two weeks to give a keynote at #ICRA2026. I'll share our recent progress on building generalist humanoid robots and show some of the latest results.

Check out my talk on June 3: https://t.co/DTovfYLb6v

[Sapient Intelligence @Sapient_Int]

Introducing HRM-Text.

An ultra-lean 1B-parameter reasoning language model designed to deliver strong general performance with a fraction of the data, compute, and infrastructure.

Trained on just 40B structured tokens, HRM-Text achieves competitive performance while using ~1/1000 of the training data of comparable models.

The kicker? The full model trains in roughly one day on a $1,000 budget.

This opens the door to a new generation of AI that is powerful, accessible, and radically easier to adapt. Theories and research concepts once deemed too expensive to test are officially back in the game.

Sapient Intelligence invites you to help us shape a new paradigm for general intelligence.

[Yu Lei @_OutofMemory_]

@wenhaocha1 @thoma_gu Deeply supervised(aligned) network

[Figure @Figure_robot]

We taught two F.03 robots to clean a room and make a bed in under 2 minutes - fully autonomous.

[Yu Lei @_OutofMemory_]

@alpercanbe A culture.

[Yu Lei @_OutofMemory_]

Love this.

[Danfei Xu @danfei_xu]

Submit your CoRL workshop proposal! This year @RLioutikov and I wanted to make the workshop more "workshopy".

Main changes are:
- Half-day events only
- Limited speaker slots
- Challenge- and participation-driven
- A post-workshop artifact (white paper, report, paper, etc.) summarizing the discussions

[Zhengyi “Zen” Luo @zhengyiluo]

Open-sourcing the whole package here!

The last piece of our SONIC open-source, data collection, gr00t VLA post-training, inference just hit the repo!

Train your Autonomous policies on G1 Whole-body with SONIC and gr00t N1.7!

🧑‍💻Code: https://t.co/7u3SBxzXU9
📑Docs: https://t.co/NhwlZtRqUu