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About this paper
Alexander Pashevich, Robin Strudel, Igor Kalevatykh, Ivan Laptev, Cordelia Schmid
This research introduces a method for automatically learning optimal image augmentation strategies specifically for synthetic depth images to improve Sim2Real policy transfer in robotics. This approach enables training robust manipulation policies entirely in simulation, achieving high success rates on real-world picking, stacking, and placing tasks without real robot fine-tuning.
Key insights
- An automated, learned approach to depth image augmentation significantly outperforms manually designed or naive randomization for bridging the Sim2Real gap.
- Optimizing for a computationally inexpensive proxy task (object position estimation) reliably predicts and translates to improved performance in complex real-world robotic manipulation tasks.
- The learned augmentation functions are policy-independent and generalize effectively across different manipulation tasks and to novel, unseen object instances, capturing fundamental visual domain discrepancies.
Summary by alphaXiv.
Tip: change arxiv.org to autoarxiv.org in any arXiv abstract link to open the paper here.