Learning to Manipulate Anywhere: A Visual Generalizable Framework For
  Reinforcement Learning
Authors: name: Zhecheng Yuan, name: Tianming Wei, name: Shuiqi Cheng, name: Gu Zhang, name: Yuanpei Chen, name: Huazhe Xu

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/.