Reproduce “ArtVIP: Articulated Digital Assets of Visual Realism, Modular Interaction, and Physical Fidelity for Robot Learning” on OpenResearch
Turn your favorite coding agents into research agents that review literature, develop hypotheses, and run experiments.
Universal app for Apple silicon and Intel. Requires macOS 11 or later.
About this paper
Zhao Jin, Zhengping Che, Tao Li, Zhen Zhao, Kun Wu, Yuheng Zhang, Yinuo Zhao, Zehui Liu and 5 more
The ArtVIP dataset introduces a collection of open-source, high-fidelity articulated digital assets, complemented by indoor scenes and pixel-level annotations, specifically designed to advance robot learning. These assets achieve superior visual realism, physical fidelity, and modular interaction capabilities, demonstrating improved zero-shot sim-to-real transfer and higher success rates in robot manipulation tasks compared to existing datasets.
Key insights
- High-quality digital assets, meticulously crafted with visual realism and physical fidelity, are crucial for effectively reducing the sim-to-real gap in robot learning.
- Detailed and physically accurate modeling of joint dynamics, including position- and velocity-dependent parameters, is essential for reproducing complex real-world interactions in simulation.
- Embedding modular interaction behaviors directly into assets significantly streamlines development workflows and accelerates algorithm iteration by making assets inherently interactive and reusable.
Summary by alphaXiv.
Tip: change arxiv.org to autoarxiv.org in any arXiv abstract link to open the paper here.