DREAM: Differentiable Real-to-Sim-to-Real Engine for Learning Robotic Manipulation

Abstract

We introduce DREAM, a differentiable real-to-sim-to-real engine for robotic manipulation that learns from real data, adapts a simulation model, and transfers policies back to hardware through differentiable optimization.

Haozhe Lou

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Mingtong Zhang

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Haoran Geng

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Hanyang Zhou

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Sicheng He

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Zhiyuan Gao
Zhiyuan Gao
PhD Student@USC

Creating digital doubles with the power of differentiable everything

Siheng Zhao

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Jiageng Mao

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Pieter Abbeel

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Jitendra Malik

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Daniel Seita

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Yue Wang

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