Luai Abuelsamen

robotics @ucberkeley swe @tesla

[Luai Abuelsamen @luailol]

Paper readings as a post work activity is wild

[DailyPapers @HuggingPapers]

MolmoAct2 by AllenAI

A fully open action reasoning model for real-world robot deployment, featuring the largest open bimanual dataset (720 hours), a specialized spatial reasoning backbone, and adaptive-depth reasoning that cuts latency while outperforming GPT-5 and Gemini Robotics.

[dar @radbackwards]

New 1X production numbers just came in… I’m shaking. So proud of this team.

[Kyle🤖🚀🦭 @KyleMorgenstein]

steal the idea:
- video model —> multiple imagined future trajectories (1X style)
- multiple inverse dynamics module rollouts
- score based on likelihood
- relax the perfect tracking assumption
- profit

[Younghyo Park @younghyo_park]

What's different between these two BC policies? It's the same architecture, training budget, and data collection setup — the only difference is the controller gains!

Controller gains are an understudied design parameter in robot learning. In our new work (w/ @BronarsToni*, @pulkitology), we show how they act as an inductive bias across BC, RL, and Sim2Real transfer, with real consequences on performance. Here's what we found 🧵

* Equal Contribution
📄arxiv: https://t.co/SMYgh7i8cA
🔗website: https://t.co/cLCd1FYCdJ

[Jacob Zietek @JacobZietek]

*per captia

https://t.co/v31auM0AUw

[Luai Abuelsamen @luailol]

@JacobZietek Says you

[Luai Abuelsamen @luailol]

@WilliamVerst @Tesla_AI @UMA_Robots Best of luck William!

[Physical Intelligence @physical_int]

We developed an RL method for fine-tuning our models for precise tasks in just a few hours or even minutes. Instead of training the whole model, we add an “RL token” output to π-0.6, our latest model, which is used by a tiny actor and critic to learn quickly with RL.

[Ryohei Sasaki@engineer @rsasaki0109]

ACG: Action Coherence Guidance for Flow-based Vision-Language-Action Models (ICRA 2026)
https://t.co/JVzPkA0X3R
Action Coherence Guidance (ACG) is a training-free, test-time guidance algorithm that improves temporal and spatial action consistency in Vision-Language-Action (VLA) models. It mitigates motion jitter, unintended pauses, and trajectory drift caused by noisy demonstrations, resulting in stable and precise robotic manipulation.

[Zhanyi Sun @s_zhanyi]

We find that RL post-training can substantially improve BC policies without teaching them anything fundamentally new.

So what is RL doing? In DICE-RL, it contracts a broad behavior prior toward high-value modes. (1/n)

https://t.co/5WbSgSQ5Ok

[Junfan Zhu 朱俊帆 ✈️ CVPR @junfanzhu98]

@ycombinator @GoogleDeepMind @trychroma @livekit @browserbase Multimodal Frontier Hackathon Recap🔥
@NanoBanana, @antigravity, @stitchbygoogle, @GoogleAI, @GoogleAIStudio, @FlowbyGoogle

More fun pics on LinkedIn!
https://t.co/0JZQF3cInl

[Learning Systems and Robotics Lab (is hiring!) @learnsyslab]

Our work, "A Primer on SO(3) Action Representations in Deep Reinforcement Learning," was accepted to #ICLR2026! We provide a systematic study of action representation choices in RL, showing that they fundamentally impact training stability and performance.

#Robotics #AI #RL

[ITS Berkeley @BerkeleyITS]

We wrapped up #TRBAM this afternoon with three poster sessions by @cal_engineer @berkeley_cee students Jingchun Wang and Yi Ju; @berkeleymechanicalengineering recent MEng grad Luai Abuelsamen; and PATH's Hao Liu; and a lectern session by TE PhD student Tyler Truksa

[Luai Abuelsamen @luailol]

Excited to be presenting my poster at TRB tomorrow (A278)

[ITS Berkeley @BerkeleyITS]

TRB is almost here! UC Berkeley ITS and Berkeley Lab researchers head to Washington, D.C. for the Transportation Research Board Annual Meeting #TRBAM (Jan. 11–15, 2026).
Check out the ITS TRB Schedule: https://t.co/Pt6mENgraY

[a16z @a16z]

The physical AI deployment gap

a16z's Oliver Hsu on the gap between the frontier of robotics research and the deployment of robots in production settings, and why it matters: https://t.co/7aSVPIQ5zb

[Ilir Aliu @IlirAliu_]

Robots assembling real products without scripts or human demos just became real.

Fabrica is a dual-arm system from MIT that can plan and execute multi-part assembly tasks end-to-end.

• Plans the full assembly hierarchy
precedence, sequence, grasp, motion, even fixture generation
• Combines planning with lightweight reinforcement learning for contact-rich steps
• Trains generalist policies that transfer zero-shot to real robots
• Achieves 80% success on complex multi-part assemblies ❗️
• Works across object shapes, grasp poses, and assembly directions

Most assembly systems break once geometry changes.

This shows that general multi-part assembly can be solved without handcrafted rules or human demonstrations.

Robots building unfamiliar objects in the real world.

Thanks for sharing, @YungshengTian!

📍Paper
https://t.co/IvtXqDZeuH

📍Project
https://t.co/Xxkze1oxW8

——

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[Luai Abuelsamen @luailol]

Hoomanoid summit

[Sunday @sundayrobotics]

After 18 months in stealth, dozens of prototypes, millions of real-home demonstrations, and one final all-nighter, we’re thrilled for you to say hello to Memo

[Jia-Bin Huang @jbhuang0604]

POV: reading robot learning papers