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Yixuan Wang ✈️ CoRL

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@YXWangBot

New York, USA wangyixuan12.github.io Joined October 2019
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Yixuan Wang ✈️ CoRL Verified account @YXWangBot
1/ World models are getting popular in robotics 🤖✨
But there’s a big problem: most are slow and break physical consistency over long horizons.

2/ Today we’re releasing Interactive World Simulator:
An action-conditioned world model that supports stable long-horizon interaction.

3/ Key result:
✅ 10+ minutes of interactive prediction
✅ 15 FPS
✅ on a single RTX 4090🔥

4/ Why this matters: it unlocks two critical robotics applications:
🚀 Scalable data generation for policy training
🧪 Faithful policy evaluation

5/ You can play with our world model NOW at yixuanwang.me/interactive_wo…. NO git clone, NO pip install, NO python. Just click and play!

NOTE ⚠️
ALL videos here are generated purely by our model in pixel space! They are **NOT** from a real camera

More details coming 👇 (1/9)

#Robotics #AI #MachineLearning #WorldModels #RobotLearning #ImitationLearning
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Yixuan Wang ✈️ CoRL Verified account @YXWangBot
An interactive 3D world simulator just dropped!

This world simulator operates in a unified 3D representation space spanning point clouds, voxels, and 3D Gaussians, and renders multi-view consistent RGB-D images.

Try the interactive 3D demo on our website - you can drag both the gripper and the camera around. The cloth dynamics are insanely good! You can stretch, crumple, and even drop the cloth. Check it out!
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Jimmy Wang Verified account @jwang771 5d
We introduce 3D World Simulator, an interactive world model that runs at 50+ FPS on an RTX 5090.

Our model predicts in a unified 3D representation space, capable of generating:
• Novel view appearance
• Multi-view consistent RGB-D
• 3D geometry
• Spatially aligned tactile signals

Play with the world model now at jwang771.github.io/3dworldsimulat…!
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2 replies 10 reposts 85 likes 5.7K views View on X
Yixuan Wang ✈️ CoRL Verified account @YXWangBot
One of most common questions I was asked about IWS is generalization. I am super excited and glad to see that this world model can generalize sooooo well using only robot data, with careful context design, right execution, and of course data + compute. Fantastic work!!!
Yiming Dou Verified account @_YimingDou Oct 29
Introducing DreamFAR, a world model trained from scratch using only robot data, without internet pretraining. It supports:
🎮 Real-time teleoperation
⏱️ Minutes of long-horizon rollout
🧩 Generalization across 1,000+ tasks

yimingdou.com/DreamFAR

🧵 (1/8)
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2 replies 2 reposts 26 likes 1.8K views View on X
Yixuan Wang ✈️ CoRL Verified account @YXWangBot
Wow! This is quite awesome! The all in one tactile sensor with quite elegant design!
Homanga Bharadhwaj @ CoRL Verified account @mangahomanga Oct 21
Your robot's shape shouldn't limit its sense of touch.

Meet OpenTac: an open ecosystem for custom tactile skins that sense normal + shear forces.

Very excited about this collaborative project! Can’t wait to see what the community builds with it. 🧵👇

opentac.github.io
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Yixuan Wang ✈️ CoRL Verified account @YXWangBot
Check out the amazing exploration scaling work for agentic robots!!
Yilun Du Verified account @du_yilun Oct 16
Robotics has spent years scaling demonstration data and model size. What happens when we scale EXPLORATION for robot agents?

We're incredibly excited to share RAX, an agentic robotics system that learns by scaling online exploration.

A coding agent connects directly to the robot’s perception and control APIs, learns from feedback, and builds programs that perceive, plan, and act. The model stays frozen. As exploration scales, the agent improves its programs and builds up reusable skills.

The results:
• ZERO teleoperation or action demonstrations on real robots—from insertion with 0.1 mm clearance and air-fryer manipulation to deformable objects and long-horizon mobile whole-body control.
• +37 percentage points on average over leading learned policies across five simulation benchmarks: RoboDojo, BEHAVIOR-1K, HumanoidArena, DexJoCo’s bimanual tasks, and LIBERO-Pro.
• One framework spanning single-arm, bimanual, dexterous, humanoid, and mobile robots, with new embodiments connected through APIs.

The craziest part: on each real-world embodiment, going from scratch to mastering the tasks took roughly two hours. No teleop. No model finetuning.

We’re open-sourcing the full framework AND every experiment session. We can’t wait to see what people build with it!

Project + sessions: robot-agent-exploration.com
Paper: assets.robot-agent-exploration.com/paper/rax.pdf
GitHub: N

Project fully led by @RunhanH and @xczhangxx who are absolutely awesome.

How it works, experiments, and robot videos below 🧵

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