Runyi (Ingrid) Yu

PhD @ HKUST, Master @ PKU. Focused on Embodied AI research

[Runyi (Ingrid) Yu @Runyi_Ingrid_Yu]

🎉 Excited to share our new work: OmniContact🎉

We introduce a framework built on "Contact Flow" to tackle the challenges of generalizable loco-manipulation and long-horizon task planning for 🤖.

✅ Code, Models, and dataset are all avaliable!

🌐: https://t.co/jovO19xQv2

[Yi Shi @YiShi_333]

GPC turns hours of human motion into a reusable vocabulary of physical motor skills, then uses that vocabulary to control characters in real time. GPC can then be efficiently adapted to new tasks while preserving natural, robust, lifelike movement.

[Runyi (Ingrid) Yu @Runyi_Ingrid_Yu]

👏 Welcome to follow, discuss, and try it out:
📄 Paper: https://t.co/6lDyLLbxbd
🌐 Project page: https://t.co/suRCY3mdDP
💻 Code: https://t.co/QZwD5on7HT
📊 Dataset: https://t.co/vdp83jsVSy

[Runyi (Ingrid) Yu @Runyi_Ingrid_Yu]

🎉 Excited to share our new work: OmniContact🎉

We introduce a framework built on "Contact Flow" to tackle the challenges of generalizable loco-manipulation and long-horizon task planning for 🤖.

✅ Code, Models, and dataset are all avaliable!

🌐: https://t.co/jovO19xQv2

[Runyi (Ingrid) Yu @Runyi_Ingrid_Yu]

Quite impressive ！

[JunLi R @junli_r84995]

sharing our new project Smash!🏓
We built the first outdoor humanoid table tennis player with fully onboard perception — no MoCap, no external cameras. check the video for details.
paper and code on the way!

[Runyi (Ingrid) Yu @Runyi_Ingrid_Yu]

Amazing work！

[Wenjia Wang @WenjiaWang_HKU]

🚀 Excited to share our #CVPR2026 paper: EmbodMocap: In-the-Wild 4D Human-Scene Reconstruction for Embodied Agents.

EmbodMocap, a portable yet affordable solution requiring only two moving iPhones—no calibrated multi-view camera studio, motion capture suits, or LiDAR sensors needed.

With our fully automated optimization pipeline, you can effortlessly obtain high-precision scene meshes, human interaction motions, RGBD images, and camera parameters.

The captured data is ready for training human-scene reconstruction models (like TRAM, pi3, etc.) and humanoid control policies (like deepmimic, AMP, etc.).

What you need to do:
1. Borrow or buy two iPhone 12 Pros from eBay (600 USD in total).
2. Find 2 friends, then capture the sequences.
3. Deploy our repo, run our code, and get the results!

The code and data will be released within 1 week. (Just come back to work from the Chinese Spring Festival, Happy Chinese New Year!)

📷 Project page: https://t.co/1soqR44lCJ

📷ArXiv: https://t.co/F2qp2deQ2m

📷Code: https://t.co/ZvdeiA3y8Z

[Runyi (Ingrid) Yu @Runyi_Ingrid_Yu]

Amazing!

[Yinhuai @NliGjvJbycSeD6t]

Introduce HumanX, a full-stack framework that compiles human video into generalizable, real-world interaction skills 🏀⚽️🥊📦 for humanoids, without task-specific rewards.

Paper: https://t.co/TJR620zPGE
Page: https://t.co/zbwrHbN3Na

#humanoid #ai #hkust #robotics #sports

[Yinhuai @NliGjvJbycSeD6t]

First-ever real-world basketball demo
by a humanoid robot 🤖🏀

Bonus: I became the first person to record a block against a humanoid🤭 #Robotics #AI #TechDemo #NBA

[JunLi R @junli_r84995]

⚽️ We create a humanoid goalkeeper !
🥅One-stage RL training
⏰Fully autonomous & real-time
📷Alternative perception: MoCap ↔️ onboard camera
🔁 Generalizes to ball grabbing, squat & jump escapes
website: https://t.co/yBFT5xmiMQ
paper: https://t.co/prx9qaH3ej

[Tao Huang @TaouHuang]

💡 How can humanoids learn adaptable skills from a single human motion?
🤖 Introducing AdaMimic: Towards Adaptable Humanoid Control via Adaptive Motion Tracking

Paper: https://t.co/fJBxNPZKeF
Website: https://t.co/nVPh3yZKrf

[Runyi (Ingrid) Yu @Runyi_Ingrid_Yu]

Lifelike, generalizable humanoid-scene interactions in the wild! A big step towards real-world deployment. 🔥

[Huayi Wang @HuayiWang04]

💡 How can humanoids achieve autonomous, generalizable, and lifelike interactions with real-world objects?
🤖 Introducing PhysHSI: Towards a Real-World Generalizable and Natural Humanoid-Scene Interaction System

Paper: https://t.co/k7iykFXjn8
Website: https://t.co/eC3oQvsLKt

[Jason Peng @xbpeng4]

Implementing motion imitation methods involves lots of nuisances. Not many codebases get all the details right. So, we're excited to release MimicKit!
https://t.co/7enUVUkc3h
A framework with high quality implementations of our methods: DeepMimic, AMP, ASE, ADD, and more to come!

[Weishuai Zeng @weishuaizeng]

We are excited to re-introduce our Behavior Foundation Model for Humanoid Robots, built upon a unified perspective of diverse WBC tasks, as a promising step toward a foundation model for general humanoid control.
🔗Website: https://t.co/AMDocTClqD
📜Paper: https://t.co/ema9egIJcE

[Weishuai Zeng @weishuaizeng]

Excited to share our latest progress on building Behavior Foundation Model for Humanoid Robots🎈

Forward roll, hip-pop dance, even cartwheel -- all the things you have never imagined the little G1 could do -- we have made it based on ONE model👌

Stay tuned for paper and code😉

[Ke Fan @frankefanrank]

🚀 Our #ICCV25 paper "GO TO ZERO" is out - the first systematic exploration of zero-shot text-to-motion generation!
✅ 2M+ high-quality 3D motions
✅ New benchmark with 126 real-world prompts
✅ 7B motion foundation model
We're pushing motion generation into the zero-shot era!

[Siyuan Huang @siyuanhuang95]

🤖 Ever dreamed of controlling a humanoid robot to perform complex, long-horizon tasks — using just a single Vision Pro?

🎉 Meet CLONE: a holistic, closed-loop, whole-body teleoperation system for long-horizon humanoid control!

🏃‍♂️🧍 CLONE enables rich and coordinated interactive tasks:
🥊 boxing
🏓 table tennis
🤲 object pickup
📦 room arrangement
🤝 handover
… and more!
🌀 Our closed-loop error correction powered by LiDAR odometry ensures precision, while motion-captured demonstrations supercharge policy learning — unlocking the full potential of the G1 robot.
🎥 It’s hard to squeeze the magic into 1 minute — check out the full video demo and project page here:
🔗 https://t.co/B8npK6lGHT

[Yitang Li @li_yitang]

🤖Can a humanoid robot carry a full cup of beer without spilling while walking 🍺?

Hold My Beer !

Introducing Hold My Beer🍺: Learning Gentle Humanoid Locomotion and End-Effector Stabilization Control

Project: https://t.co/jUMwEVEyAX

See more details below👇

[Runyi (Ingrid) Yu @Runyi_Ingrid_Yu]

@liangpan_t @NliGjvJbycSeD6t Thanks a lot😭God Liang

[Runyi (Ingrid) Yu @Runyi_Ingrid_Yu]

Trapped by data quality and volume challenges in imitation learning?

Check our #SIGGRAPH2025 paper: 🤖SkillMimic-V2: Learning Robust and Generalizable Interaction Skills from Sparse and Noisy Demonstrations.

🌐 Page：https://t.co/tK6JhgD5MK
🧑🏻‍💻 Code：https://t.co/fhnKZwwcLD

[Runyi (Ingrid) Yu @Runyi_Ingrid_Yu]

@liangpan_t Good job！