Rui Yan

M.S. in ECE @UCSD | ML&DS, Robotics

[Rui Yan @Hi_Im_RuiYan]

ACE-F is finally open sourced, with a hardware assembly tutorial and teleoperation code for Franka and Xarm7 robots. Check out our website and more below!Hardware: https://t.co/2GGWuJAa52
Software: https://t.co/JWFKKxYu3e
Webpage: https://t.co/gZJkNxsfg1
Arxiv: https://t.co/3naBgeb4xp

[Rui Yan @Hi_Im_RuiYan]

Meet ACE-F — a novel, foldable teleoperation platform for collecting high-quality robot demonstration data across robot embodiments.

Using a specialized soft-controller pipeline, we interpret end-effector positional deviations as virtual force signals to provide the user with force feedback, without requiring expensive sensors! ACE-F simplifies control for a diverse array of robot platforms, making complex tasks that require dexterous manipulation highly intuitive.

Check out the project website here!
https://t.co/gZJkNxsfg1

[Rui Yan @Hi_Im_RuiYan]

@xiaolonw @Meta Congratulations!

[Isabella Liu @Isabella__Liu]

VLA/VAs are doing well on short skills like pick-and-place. But real tasks rarely stop after one action, they require 1) many interdependent steps, 2) progress tracking, and 3) recovery from mistakes.

In our paper LoHo-Manip, we address long-horizon manipulation with trace-conditioned VLA planning: a task manager tracks what’s done, plans what remains, and guides execution with visual traces.

[Rui Yan @Hi_Im_RuiYan]

Cross-embodiment learning for diverse dexterous hands is an exciting direction.
Looking forward to seeing how shared latent representations enable generalization across embodiments.

[Guangqi Jiang @LuccaChiang]

Ever want to have a single policy to control diverse robots as well as different dexterous hands, or to observe the emergent behavior under cross embodiment training?

Introducing our #CVPR2026 paper XL-VLA, Cross-Hand Latent Representation for Vision-Language-Action Models.

[Guangqi Jiang @LuccaChiang]

Ever want to have a single policy to control diverse robots as well as different dexterous hands, or to observe the emergent behavior under cross embodiment training?

Introducing our #CVPR2026 paper XL-VLA, Cross-Hand Latent Representation for Vision-Language-Action Models.

[Changwei Jing @cwj99770123]

Can we bridge the Sim-to-Real gap in complex manipulation without explicit system ID? 🤖

Presenting Contact-Aware Neural Dynamics — a diffusion-based framework that grounds simulation with real-world touch.

Implicit Alignment: No tedious parameter tuning.

Tactile-Driven: Captures non-smooth contact events.

Consistent: Stable predictions in contact-rich tasks.

[Kehlani Fay @Kehlani_Fay]

What makes robot hands dexterous? 🤖🖐️

We generate robot hands + control, sim-to-real, in under 24 hours.

Paper: Cross-Embodied Co-Design for Dexterous Hands
🔥 Rapid evaluation w/ cross-embodied policies
🦾 Open-source modular hand platform
💡 Automated full-hand generation

[Xueyan Zou @xyz2maureen]

I will join Tsinghua University, College of AI, as an Assistant Professor in the coming month. I am actively looking for 2026 spring interns and future PhDs (ping me if you are in #NeurIPS).

It has been an incredible journey of 10 years since I attended an activity organized by Tsinghua University and decided to change my undergraduate major from Economics to Computer Science, inspired by one of the teammates. During the 10 years, I met with appreciation of many wonderful researchers/professors who led me to continued growth. 🐿️

My research focus will continue to be AI & Robotics, with a specific emphasis on Interactive Embodied Intelligence. You can check my homepage to learn more: https://t.co/6hHsc62x0A.

I am currently local to San Diego and will be attending #NeurIPS. Please ping me over WeChat or Email if any old or new friends are interested in having a coffee chat! (Really looking forward to meeting as many friends as possible at #NeurIPS)

[The photo is one of the places that I will miss a lot in the US]

[Rui Yan @Hi_Im_RuiYan]

This work was made possible by massive contributions from authors @Hi_Im_RuiYan , @JiajianFu , @ShiqiYang_17 ,@PaulsenLarsE , @xuxin_cheng , @xiaolonw

[Rui Yan @Hi_Im_RuiYan]

Meet ACE-F — a novel, foldable teleoperation platform for collecting high-quality robot demonstration data across robot embodiments.

Using a specialized soft-controller pipeline, we interpret end-effector positional deviations as virtual force signals to provide the user with force feedback, without requiring expensive sensors! ACE-F simplifies control for a diverse array of robot platforms, making complex tasks that require dexterous manipulation highly intuitive.

Check out the project website here!
https://t.co/gZJkNxsfg1

[Rui Yan @Hi_Im_RuiYan]

By combining many interchangeable end-effectors, a novel control structure, and choosing inverse kinematics over joint-copy, ACE-F is highly customizable and can be used with a wide-array of follower arms!

[Rui Yan @Hi_Im_RuiYan]

ACE-F's basic control structure uses inverse kinematics to determine the end-effector error. Then, the error magnitude is used to scale the forces that the user can feel. For more, see below!

[Xiongyi Cai @XiongyiCai]

A large human behavior model.

Introducing In-N-On, our latest findings in scaling egocentric data for humanoids.

1. Pre-training and post-training with human data
2. 1,000+ hours of in-the-wild data and 20+ hours of on-task data with accurate action labels

Website: https://t.co/QOUcwiLsSJ
Arxiv: https://t.co/SML0YFBMQu

By simply scaling data, our robot can follow novel language instruction. Check out the 🧵

[Xiaolong Wang @xiaolonw]

Most robot learning has focused on simple position control. But think about how a human uses a wrench 🔧: you’re not just rotating in one direction—you’re continuously shaping the forces, pushing and pulling differently as you move. Our robot can do exactly that now.

[Jianglong Ye @jianglong_ye]

How do we make dexterous hands handle both power and precision tasks with ease? 🫳👌🫰

We introduce Power to Precision (💪➡️🎯), our new paper that optimizes both control and fingertip geometry to unlock robust manipulation from power grasp to fine-grained manipulations.

With simplified finger motions and augmented fingertips, the hand can perform diverse motions from pinching a nut🔩 to handling a pan🍳. Check the demos below🎥.

[Guangqi Jiang @LuccaChiang]

Ever want to enjoy all the privileged information in sim while seamlessly transferring to the real world? How can we correct policy mistakes after deployment?

👉Introducing GSWorld, a real2sim2real photo-realistic simulator with interaction physics with fully open-sourced code.

[Ruihan Yang @RchalYang]

How can we leverage diverse human videos to improve robot manipulation?

Excited to introduce EgoVLA — a Vision-Language-Action model trained on egocentric human videos by explicitly modeling wrist & hand motion. We build a shared action space between humans and robots, enabling seamless transfer. With some robot demos, EgoVLA becomes a powerful, generalizable robot policy.

[Xuxin Cheng @xuxin_cheng]

AMO live demo at #RSS2025 !
👉https://t.co/VKfGesu7ZA

[Rui Yan @Hi_Im_RuiYan]

@RoboticsSciSys Co-led by @JiajianFu & @ShiqiYang_17 with Lars Paulsen, @xuxin_cheng, @xiaolonw

#RSS2025

[Rui Yan @Hi_Im_RuiYan]

🚀 Meet ACE-F — a next-gen teleop system merging human and robot precision.
Foldable, portable, cross-platform — it enables 6-DoF haptic control for force-aware manipulation.

🦾 See our demo & talk at the Robot Hardware-Aware Intelligence workshop this Wed @RoboticsSciSys!