Revisiting Distribution Matching for State-to-Vision Policy Distillation in Laparoscopic Robotic Manipulation

The paper proposes a method for distilling a vision‑based policy from a state‑based teacher policy in laparoscopic robotic manipulation. By using privileged state observations as a teacher, the vision policy learns from RGB images and proprioception, requiring fewer expert demonstrations than imitation learning and outperforming pure reinforcement learning. Experiments in simulation show that this distillation approach yields more consistent and stable performance across various surgical tasks.

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• reinforcement-learning
• robotics
• efficiency