🚀 go-pugleaf

https://www.science.org/doi/10.1126/scirobotics.adt5254

| Research on autonomous surgery has largely focused on simple task
| automation in controlled environments. However, real-world surgical
| applications demand dexterous manipulation over extended durations and
| robust generalization to the inherent variability of human tissue.
| These challenges remain difficult to address using existing logic-
| based or conventional end-to-end learning strategies. To address this
| gap, we propose a hierarchical framework for performing dexterous,
| long-horizon surgical steps. Our approach uses a high-level policy for
| task planning and a low-level policy for generating low-level
| trajectories. The high-level planner plans in language space,
| generating task-level or corrective instructions that guide the robot
| through the long-horizon steps and help recover from errors made by
| the low-level policy. We validated our framework through ex vivo
| experiments on cholecystectomy, a commonly practiced minimally
| invasive procedure, and conducted ablation studies to evaluate key
| components of the system. Our method achieves a 100% success rate
| across eight different ex vivo gallbladders, operating fully
| autonomously without human intervention. The hierarchical approach
| improved the policy’s ability to recover from suboptimal states that
| are inevitable in the highly dynamic environment of realistic surgical
| applications. This work demonstrates step-level autonomy in a surgical
| procedure, marking a milestone toward clinical deployment of
| autonomous surgical systems.
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"Möchte kurz angeben, hab gerade meine Bachelorarbeit mit ner 1.0
bestanden. Bin jetzt valide Expertin für kapitalismuskritische
frühkindliche Bildung, falls ihr sowas mal braucht."
https://x.com/jetteniz/status/1483479231384506368