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OASIS is a simulation-data-driven framework for humanoid loco-manipulation that uses 3D generative models and hierarchical visuomotor policies. It achieves better zero-shot performance than real-robot training by leveraging domain randomization in simulation.
Introduces StereoPolicy, a framework that leverages synchronized stereo image pairs to improve geometric reasoning for robot manipulation policies, avoiding the fragility of RGB-D and point clouds. It integrates with diffusion-based and vision-language-action policies, showing consistent improvements in simulation and real-world tasks.