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OVIP-SG outperforms existing frameworks by preserving small object instances and enhancing retrieval accuracy, revolutionizing the mapping of fine-grained objects in 3D environments.
Simply adding multi-view videos to latent action models doesn't guarantee 3D awareness; LAWM-3D reveals the critical design choices needed for success.
Grounded language comprehension, rather than free-form reasoning, is the key to unlocking superior performance in Vision-Language-Action models.
MVUCF achieves a remarkable 98.9% on LIBERO, showcasing a 22.4-point improvement on LIBERO-Plus and a 23.3-point increase in task success rates, all without additional inference costs.
