Learning to Manipulate Anywhere: A Visual Generalizable Framework For Reinforcement Learning

Zhecheng Yuan, Tianming Wei, shuiqi cheng, Gu Zhang, chen, Huazhe Xu

Learning to Manipulate Anywhere: A Visual Generalizable Framework For Reinforcement Learning: 14 upvotes on Hugging Face Daily Papers, #8 of 13 papers on 2024-07-25. Day-by-day upvote history.

Can we endow visuomotor robots with generalization capabilities to operate in diverse open-world scenarios? In this paper, we propose Maniwhere, a generalizable framework tailored for visual reinforcement learning, enabling the trained robot policies to generalize across a combination of multiple visual disturbance types. Specifically, we introduce a multi-view representation learning approach fused with Spatial Transformer Network (STN) module to capture shared semantic information and correspondences among different viewpoints. In addition, we employ a curriculum-based randomization and augmentation approach to stabilize the RL training process and strengthen the visual generalization ability. To exhibit the effectiveness of Maniwhere, we meticulously design 8 tasks encompassing articulate objects, bi-manual, and dexterous hand manipulation tasks, demonstrating Maniwhere's strong visual generalization and sim2real transfer abilities across 3 hardware platforms. Our experiments show that Maniwhere significantly outperforms existing state-of-the-art methods. Videos are provided at https://gemcollector.github.io/maniwhere/.

Paper page on Hugging Face · arXiv

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