  Automatic laparoscope motion control is fundamentally important for surgeons
to efficiently perform operations. However, its traditional control methods
based on tool tracking without considering information hidden in surgical
scenes are not intelligent enough, while the latest supervised imitation
learning (IL)-based methods require expensive sensor data and suffer from
distribution mismatch issues caused by limited demonstrations. In this paper,
we propose a novel Imitation Learning framework for Laparoscope Control (ILLC)
with reinforcement learning (RL), which can efficiently learn the control
policy from limited surgical video clips. Specially, we first extract surgical
laparoscope trajectories from unlabeled videos as the demonstrations and
reconstruct the corresponding surgical scenes. To fully learn from limited
motion trajectory demonstrations, we propose Shape Preserving Trajectory
Augmentation (SPTA) to augment these data, and build a simulation environment
that supports parallel RGB-D rendering to reinforce the RL policy for
interacting with the environment efficiently. With adversarial training for IL,
we obtain the laparoscope control policy based on the generated rollouts and
surgical demonstrations. Extensive experiments are conducted in unseen
reconstructed surgical scenes, and our method outperforms the previous IL
methods, which proves the feasibility of our unified learning-based framework
for laparoscope control.
