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Despite advances in AI-driven surgical skill assessment, reliably tracking hands and tools in open surgery videos remains a surprisingly difficult problem, hindering motion-based analysis.
Multimodal behavioral cloning fails in surprising ways: latent-variable policies can't balance regularization, while action-space generative policies are fundamentally limited by the smoothness of their action mappings.
LAR-MoE unlocks structured expert specialization in robotic imitation learning from unlabeled demonstrations, rivaling supervised methods without needing manual phase annotations.
Ditch the da Vinci: this open-source surgical robotics platform brings precision and flexibility to autonomous laparoscopic procedures using standard industrial robots.
