# StereoEngine

Large-scale robot datasets are overwhelmingly monocular and single-arm, and capturing bimanual stereo demonstrations would need stereo rigs plus re-teleoperation for every task. StereoEngine offers two complementary routes: when a real monocular video exists, it warps the left view into the right view using monocular depth and inpaints the regions the left camera never observed with a video diffusion model; when no real video exists, it renders a stereo pair in simulation and generates both photorealistic views jointly by tiling them into one frame. On an AgileX PiPER bimanual platform, mid-training or fine-tuning policies on this synthetic data succeeds on 50/60 trials across three tasks versus 36/60 without mid-training, reaching 18/20 on transparent-bottle grasping.

Large-scale robot datasets are overwhelmingly monocular and single-arm, and capturing bimanual stereo demonstrations would need stereo rigs plus re-teleoperation for every task. StereoEngine offers two complementary routes: when a real monocular video exists, it warps the left view into the right view using monocular depth and inpaints the regions the left camera never observed with a video diffusion model; when no real video exists, it renders a stereo pair in simulation and generates both photorealistic views jointly by tiling them into one frame. On an AgileX PiPER bimanual platform, mid-training or fine-tuning policies on this synthetic data succeeds on 50/60 trials across three tasks versus 36/60 without mid-training, reaching 18/20 on transparent-bottle grasping.