PhD @GeorgiaTech | Robotics
San Diego, CA
Joined December 2024
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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: github.com/ACEFoldable/acef_…
Software: github.com/ACEFoldable/acef_…
Webpage: acefoldable.github.io/
Arxiv: arxiv.org/abs/2511.20887
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!
acefoldable.github.io/
Rui Yan retweeted
Find it. Reach it. Grasp it. Easy for us, hard for a humanoid.
Introducing HuG (#CoRL2026): Learning Humanoid Whole-Body Affordance-Aware Grasping with Active Perception.
The robot looks around for a half-hidden object, squats or stretches its whole body to reach it, and grasps. Zero real demos, zero-shot to unseen objects and everyday scenes, from low tables to high shelves.
🌐 xuanbinpeng.com/hug/
Rui Yan retweeted
Excited to share OpenRoboKit, our series of open-source robotics projects! 🤖
- RoboKit for fast, scalable optimization & simulation github.com/openrobokit/robok…
Our RoboKit paper has been accepted for a spotlight at #CORL2026
- RetargetingKit for motion transfer & teleoperation github.com/openrobokit/retar…
- GraspKit for end-to-end grasping github.com/openrobokit/grasp…
Website: openrobokit.com
Check the video (sound on 🎵) for a 16-hour grasping time-lapse in beautiful San Diego, plus a quick look at dexterous retargeting and robot simulation.
Rui Yan retweeted
We introduce RoboSkills: a minimal, low-cost skillset for agentic robotics 🤖
It supports diverse robotics workflows, such as learning cat’s cradle 🧶 from a human video (Real2Sim) and transferring multi-person motion to characters and humanoids 💃 (Motion Generation).
We also support Auto Research, Agent as Policy, and Agentic Simulation. With suitable tools and workflows, we enable cheaper models to solve complex tasks.
GitHub: github.com/openrobokit/robos…
Website: openrobokit.com/roboskills
Watch the video for more examples.
Rui Yan retweeted
Excited to share RoboSkills 🤖! A minimal, low-cost skillset for Real2Sim, Agent-as-Policy, Agentic Simulation, and much more.
Its lightweight design enables lower overhead, faster iteration, and greater flexibility in agentic robotics development. 🚀
We introduce RoboSkills: a minimal, low-cost skillset for agentic robotics 🤖
It supports diverse robotics workflows, such as learning cat’s cradle 🧶 from a human video (Real2Sim) and transferring multi-person motion to characters and humanoids 💃 (Motion Generation).
We also support Auto Research, Agent as Policy, and Agentic Simulation. With suitable tools and workflows, we enable cheaper models to solve complex tasks.
GitHub: github.com/openrobokit/robos…
Website: openrobokit.com/roboskills
Watch the video for more examples.
Rui Yan retweeted
Can we scale robot learning without scaling human labor? 📈
Demonstrations, resets, failure recovery, constant supervision—collecting robot data still takes substantial human effort.
🔑 Three keys let agents take on more of the work:
1. Real2Sim2Real: Agents explore and learn in digital twins, where trial and error is safer and cheaper.
2. Autonomous Recovery: Robots learn to recover from unexpected failures instead of waiting for human resets.
3. Human-on-Demand Learning: Multiple robots run in parallel, and humans step in only when autonomous recovery falls short.
🥑 Recova brings these together to get more robot learning out of every hour of human effort.
research.nvidia.com/labs/gea…
Joint work with @anjjei, Johan Bjorck, @ZhidingYu, @yin_hongxu, @jankautz, @DrJimFan, @yukez, and @Sifei30488L.
👇 Here's how it works 🧵
Rui Yan retweeted
No train. No teleop. No pre-built map. ASENA keeps self-evolving ↺ with every task.
With ASENA, we put coding agents in the engineer's seat: they break down tasks, write and execute robot programs, diagnose failures, and turn experience into reusable skills.
The robot doesn't just complete tasks. It builds new capabilities for the next one.
Our G1 robot found its way to the pantry, picked up a Pringles can, and handed it over. No pre-built map. No training for this task.
Behind it is a coding agent, writing, running, and fixing the robot's programs as it goes. And it self-evolves: what works is saved as code for the next task, no retraining needed. This is ASENA.
Joint work with @Isabella__Liu, @edmund_bu, Johan Bjorck, @yin_hongxu, @zhengyiluo, @jankautz, @DrJimFan, @yukez, and @Sifei30488L. 🧵👇
Rui Yan retweeted
Our G1 robot found its way to the pantry, picked up a Pringles can, and handed it over. No pre-built map. No training for this task.
Behind it is a coding agent, writing, running, and fixing the robot's programs as it goes. And it self-evolves: what works is saved as code for the next task, no retraining needed. This is ASENA.
Joint work with @Isabella__Liu, @edmund_bu, Johan Bjorck, @yin_hongxu, @zhengyiluo, @jankautz, @DrJimFan, @yukez, and @Sifei30488L. 🧵👇
Rui Yan retweeted
Human demonstrations are a scalable source of contact rich manipulation data, and they naturally record contact forces. But robots still can’t execute them directly.
Introducing WT-UMI: Tactile-based Whole-Body Manipulation via Force-Supervised Contact-Aware Planning. Accepted to #CoRL2026 ! 🎉
wt-umi.github.io/WTUMI/
Rui Yan retweeted
Transformer Transformer will be presented at #CoRL2026 🚀 See y'all in Austin!
Website: transformer-transformer.gith…
Video: youtube.com/watch?v=TTyjvPVF…
Paper: transformer-transformer.gith…
Rui Yan retweeted
Children learn to walk by falling. Robots fail too—but we usually throw those failures away.
Yet failed rollouts tell us something successful demonstrations cannot: what bad actions actually lead to.
So, can robots learn from their failures too?
Introducing FACT: Failure-Aware Causal Training for World-Action Models.
🔗website: fact-wam.github.io/
📄arxiv: arxiv.org/abs/2608.10232
💻 code: github.com/Bariona/FACT
Rui Yan retweeted
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.
Cross-embodiment learning for diverse dexterous hands is an exciting direction.
Looking forward to seeing how shared latent representations enable generalization across embodiments.
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.
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Rui Yan retweeted
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.
Rui Yan retweeted
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.
Rui Yan retweeted
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
Rui Yan retweeted
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: maureenzou.github.io/lab.htm….
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]
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!
acefoldable.github.io/
This work was made possible by massive contributions from authors @Hi_Im_RuiYan , @JiajianFu , @ShiqiYang_17 ,@PaulsenLarsE , @xuxin_cheng , @xiaolonw
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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!
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