mlsanityData to 14 Oct 2026

Robot Learning from Any Images

arXiv 2509.22970 ↗First posted 26 Sep 2025cs.RO cs.CV cs.LGcited by 7

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Abstract (arXiv)

We introduce RoLA, a framework that transforms any in-the-wild image into an interactive, physics-enabled robotic environment. Unlike previous methods, RoLA operates directly on a single image without requiring additional hardware or digital assets. Our framework democratizes robotic data generation by producing massive visuomotor robotic demonstrations within minutes from a wide range of image sources, including camera captures, robotic datasets, and Internet images. At its core, our approach combines a novel method for single-view physical scene recovery with an efficient visual blending strategy for photorealistic data collection. We demonstrate RoLA's versatility across applications like scalable robotic data generation and augmentation, robot learning from Internet images, and single-image real-to-sim-to-real systems for manipulators and humanoids. Video results are available at https://sihengz02.github.io/RoLA .

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 55 of its 65 references that are in the map. First posted on arXiv 26 Sep 2025.

Builds on

55 of 65 references.

References · 55

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38 more
  1. 2018Gibson Env: Real-World Perception for Embodied Agentscited by 481
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  19. 2025PhysTwin: Physics-Informed Reconstruction and Simulation of Deformable Objects from Videoscited by 87
  20. 2025Phantom: Training Robots Without Robots Using Only Human Videoscited by 70
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  23. 2024RAM: Retrieval-Based Affordance Transfer for Generalizable Zero-Shot Robotic Manipulationcited by 58
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  25. 2023cuRobo: Parallelized Collision-Free Minimum-Jerk Robot Motion Generationcited by 52
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  38. 2025VR-Robo: A Real-to-Sim-to-Real Framework for Visual Robot Navigation and Locomotioncited by 22

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 4; 2026: 3.

7 papers.