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

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arxiv 2407.15815 v2 pith:4YL7SYKG submitted 2024-07-22 cs.RO cs.AIcs.CV

Zhecheng Yuan , Tianming Wei , Shuiqi Cheng , Gu Zhang , Yuanpei Chen , Huazhe Xu This is my paper · ORCID
classification cs.ROcs.AIcs.CV
keywords maniwherevisuallearninggeneralizationacrossapproachframeworkgeneralizable
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Can we endow visuomotor robots with generalization capabilities to operate in diverse open-world scenarios? In this paper, we propose \textbf{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/.

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Forward citations

Cited by 10 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. HALO-WA: Hybrid-Attention Latent-Guided Online Reinforcement Learning for World-Action Models

    cs.RO 2026-07 conditional novelty 6.0 of 10

    A hybrid-attention latent-guided online RL adapter lifts frozen world-action models from 26.4% to 87.1% average success on four real precision manipulation tasks in 45–75 minutes each.

  2. DeGuV: Depth-Guided Visual Reinforcement Learning for Generalization and Interpretability in Manipulation

    cs.RO 2025-09 conditional novelty 6.0 of 10

    Depth-guided masking improves visual RL generalization, sample efficiency, and interpretability on manipulation tasks.

  3. H$^3$DP: Triply-Hierarchical Diffusion Policy for Visuomotor Learning

    cs.RO 2025-05 conditional novelty 6.0 of 10

    H3DP couples depth-layered, multi-scale visual features to coarse-to-fine denoising stages, reporting a +27.5% relative success-rate improvement over DP3 across 44 simulation tasks.

  4. Merging and Disentangling Views in Visual Reinforcement Learning for Robotic Manipulation

    cs.LG 2025-05 conditional novelty 6.0 of 10

    By summing multi-view features and adding single-view features as actor-critic augmentations, MAD produces manipulation policies that learn faster and tolerate missing cameras in simulation.

  5. Novel Demonstration Generation with Gaussian Splatting Enables Robust One-Shot Manipulation

    cs.RO 2025-04 conditional novelty 6.0 of 10

    RoboSplat edits 3D Gaussian scene reconstructions to synthesize diverse robot demonstrations from one expert trajectory, and behavior-cloned policies trained on this data generalize robustly across six disturbance typ...

  6. Wheeled Lab: Modern Sim2Real for Low-cost, Open-source Wheeled Robotics

    cs.RO 2025-02 conditional novelty 6.0 of 10

    Wheeled Lab is an open-source ecosystem that trains three zero-shot RL policies on low-cost wheeled robots in Isaac Lab and deploys them in the real world.

  7. DenseMatcher: Learning 3D Semantic Correspondence for Category-Level Manipulation from a Single Demo

    cs.RO 2024-12 conditional novelty 6.0 of 10

    DenseMatcher combines 2D image features with a 3D neural network and functional maps to compute dense semantic correspondences between textured 3D objects, enabling single-demo cross-category robot manipulation.

  8. Diffusion-VLA: Generalizable and Interpretable Robot Foundation Model via Self-Generated Reasoning

    cs.RO 2024-12 conditional novelty 6.0 of 10

    A robot policy generates its own language reasoning before acting and injects it into a diffusion action decoder, outperforming several VLA baselines on real-robot manipulation.

  9. RoboPearls: Editable Video Simulation for Robot Manipulation

    cs.CV 2025-06 conditional novelty 5.0 of 10

    RoboPearls is a 3D Gaussian Splatting based framework that edits demonstration videos into varied photorealistic simulations, and training on them improves robot manipulation success rates on RLBench and COLOSSEUM.

  10. FunGrasp: Functional Grasping for Diverse Dexterous Hands

    cs.RO 2024-11 conditional novelty 5.0 of 10

    Given one RGBD image of a human grasping an object, FunGrasp retargets the grasp to several robot hands and achieves functional real-world grasping of unseen objects.

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