From Instrument-Mounted Demonstrations to In-Vivo Execution: Learning Bimanual Laparoscopic Appendectomy Without Robot-Collected Demonstrations
Most minimally invasive surgery is still performed with hand-held instruments, and the surgeon's instrument kinematics are lost when the operation ends. We present an end-to-end pipeline that captures that motion in the operating room and uses it to train a surgical robot policy: a shaft-mounted instrument-state logger, a data pipeline that measures and removes every channel’s latency, a diffusion policy selected by closed-loop rollouts in simulation, and deployment on live animals. Retrained on 849 in-vivo demonstrations, the policy completed the appendectomy in three of four live rabbits with electrosurgery armed.

