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arXiv Machine Learning By Jason Chen, I-Chun Arthur Liu, Gaurav Sukhatme, Daniel Seita

StereoEngine: Scaling Stereo Bimanual Robot Data without Stereo Cameras

Read the original on arXiv Machine Learning →

StereoEngine introduces two methods for generating realistic synthetic stereo data for robotic manipulation. The first method warps a monocular video into a right view using depth estimation and a video diffusion model to inpaint disoccluded regions, while the second method renders stereo pairs directly in simulation. These approaches outperform existing baselines in stereo quality and enable improved bimanual manipulation policies when used for mid‑training or task‑specific fine‑tuning.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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