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RoLD: Robot Latent Diffusion for Multi-task Policy Modeling

Autor: Tan, Wenhui, Liu, Bei, Zhang, Junbo, Song, Ruihua, Fu, Jianlong

Modeling generalized robot control policies poses ongoing challenges for language-guided robot manipulation tasks. Existing methods often struggle to efficiently utilize cross-dataset resources or rely on resource-intensive vision-language models, th

Externí odkaz: http://arxiv.org/abs/2403.07312

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Transferring Foundation Models for Generalizable Robotic Manipulation

Autor: Yang, Jiange, Tan, Wenhui, Jin, Chuhao, Yao, Keling, Liu, Bei, Fu, Jianlong, Song, Ruihua, Wu, Gangshan, Wang, Limin

Improving the generalization capabilities of general-purpose robotic manipulation agents in the real world has long been a significant challenge. Existing approaches often rely on collecting large-scale robotic data which is costly and time-consuming

Externí odkaz: http://arxiv.org/abs/2306.05716

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AlphaBlock: Embodied Finetuning for Vision-Language Reasoning in Robot Manipulation

Autor: Jin, Chuhao, Tan, Wenhui, Yang, Jiange, Liu, Bei, Song, Ruihua, Wang, Limin, Fu, Jianlong

We propose a novel framework for learning high-level cognitive capabilities in robot manipulation tasks, such as making a smiley face using building blocks. These tasks often involve complex multi-step reasoning, presenting significant challenges due

Externí odkaz: http://arxiv.org/abs/2305.18898

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