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Korea Robotics Society

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±¹³»Çмú´ëȸ

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- 2010. 03 ~ ÇöÀç: Á¶±³¼ö/ºÎ±³¼ö/±³¼ö, KAIST Ç×°ø¿ìÁÖ°øÇаú
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Equivariant and Diffusion-based Methods for Data-efficient Visual Robotic Manipulation Learning
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This talk explores innovative SE(3)-equivariant methods for end-to-end visual robotic manipulation learning, addressing challenges of data inefficiency and limited generalizability in conventional deep learning approaches. We introduce Equivariant Descriptor Fields (EDFs), highlighting their generative modeling, bi-equivariance, steerable representation, and locality principles. These enable EDFs to achieve remarkable data efficiency and generalizability without prior knowledge. We also show Diffusion-EDFs, integrating SE(3)-equivariance with diffusion-based generative modeling, enhancing data efficiency and requiring only 5 to 10 demonstrations for effective training and faster inference. Our methods demonstrate superior generalizability over previous diffusion-based techniques. We aim to uncover new research directions and solutions in the dynamic field of robotic manipulation learning through the synergy of equivariant methods and generative modeling.
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