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Kaifeng Zhang

@kaiwynd • 3,473 subscribers

Robotics @columbia

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Cloth simulation using NVIDIA Newton. Not perfect, but looking good!

Cloth simulation using NVIDIA Newton. Not perfect, but looking good!

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I asked Astra to express itself using a keyboard. 40 minutes, several accidental long presses, and a lot of backspace later, it learned how to type. Video at 20× speed. Watch till the end!
2:14
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I asked Astra to express itself using a keyboard. 40 minutes, several accidental long presses, and a lot of backspace later, it learned how to type. Video at 20× speed. Watch till the end!

Kaifeng Zhang

48,670 views • 1 month ago

Gave GPT-6 Astra the task to plug in an ethernet cable. It did it after 2 hours and 4 human prompt interventions. Being able to complete a task with tight tolerance and deformable objects is impressive. But I think speed is still a big problem with agentic robotic systems, since deep reasoning via 3D programs is an integral part of this pipeline, and it’s not obvious to me how that can be distilled/accelerated to a level comparable to VLAs.
1:37
kaiwynd's profile picture

Gave GPT-6 Astra the task to plug in an ethernet cable. It did it after 2 hours and 4 human prompt interventions. Being able to complete a task with tight tolerance and deformable objects is impressive. But I think speed is still a big problem with agentic robotic systems, since deep reasoning via 3D programs is an integral part of this pipeline, and it’s not obvious to me how that can be distilled/accelerated to a level comparable to VLAs.

Kaifeng Zhang

17,994 views • 1 month ago

🧵 Evaluating robot policies in the real world is slow, expensive, and hard to scale. During my internship at SceniX this summer, we had many discussions around the two key questions: how accurate must a simulator be for evaluation to be meaningful, and how do we get there? Our new framework, Real2Sim-Eval, takes a step toward that answer. By combining Gaussian Splatting for photorealistic rendering and soft-body digital twins for realistic dynamics, we make simulation predictive of real-world performance. 👉
1:26
kaiwynd's profile picture

🧵 Evaluating robot policies in the real world is slow, expensive, and hard to scale. During my internship at SceniX this summer, we had many discussions around the two key questions: how accurate must a simulator be for evaluation to be meaningful, and how do we get there? Our new framework, Real2Sim-Eval, takes a step toward that answer. By combining Gaussian Splatting for photorealistic rendering and soft-body digital twins for realistic dynamics, we make simulation predictive of real-world performance. 👉

Kaifeng Zhang

67,703 views • 11 months ago

Can we learn a 3D world model that predicts object dynamics directly from videos? Introducing Particle-Grid Neural Dynamics: a learning-based simulator for deformable objects that trains from real-world videos. Website: ArXiv: Code: Demo: To appear at #RSS2025
0:34
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Can we learn a 3D world model that predicts object dynamics directly from videos? Introducing Particle-Grid Neural Dynamics: a learning-based simulator for deformable objects that trains from real-world videos. Website: ArXiv: Code: Demo: To appear at #RSS2025

Kaifeng Zhang

46,516 views • 1 year ago

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