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Introducing ✨RigidFormer: Learning Rigid Dynamics with Transformers - our attempt to scale learning-based physical dynamics with Transformers. RigidFormer learns rigid dynamics with Transformers. It is a mesh-free, object-centric Transformer for multi-object rigid-body contact dynamics from point clouds. Learning physics with purely neural simulators, without relying on traditional physics engines,...

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Zhiyang (Frank) Dou

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582,402 просмотров • 5 месяцев назад •via X (Twitter)

ОбразованиеНаука и технологии
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Комментарии: 8

Фото профиля Zhiyang (Frank) Dou
Zhiyang (Frank) Dou5 месяцев назад

One direction we are especially excited about is extending this type of dynamics modeling with richer action conditioning (I showcased action-condition dynamics modeling for humanoid robots in the video). We believe that such object-centric neural dynamics models can serve as a useful building block for future world models, where physical interaction, control signals, and scene-level/object-level dynamics are learned jointly from observations in a scalable way.

Фото профиля Yushi LAN
Yushi LAN5 месяцев назад

Impressive work!

Фото профиля Zhiyang (Frank) Dou
Zhiyang (Frank) Dou5 месяцев назад

@GROS17121524 Thank you, Yushi!

Фото профиля Nurvai - The Data Layer for Physical AI
Nurvai - The Data Layer for Physical AI5 месяцев назад

Mesh free, object centric dynamics feels like a really important direction for scalable learned physics. Interesting that transformers are starting to compete with graph approaches here. How far do you think this can scale before physical consistency becomes harder to preserve?

Фото профиля Arnon ∈ ℝ³
Arnon ∈ ℝ³5 месяцев назад

Amazing work, physical understanding is the way forward

Фото профиля Shilong Liu
Shilong Liu5 месяцев назад

impressive!

Фото профиля Zhiyang (Frank) Dou
Zhiyang (Frank) Dou5 месяцев назад

@atasteoff Thanks, Shilong!

Фото профиля EB1A Experts
EB1A Experts5 месяцев назад

Impressive work!

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