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Recent advances in reinforcement learning (RL) have increased the promise of introducing cognitive assistance and automation to robot-assisted laparoscopic surgery (RALS). However, progress in algorithms and methods depends on the availability of standardized learning environments that represent skills relevant to RALS. We present LapGym, a framework for building RL environments for RALS that models the challenges posed by surgical tasks, and sofa env, a diverse suite of 12 environments. Motivated by surgical training, these environments are organized into 4 tracks: Spatial Reasoning, Deformable Object Manipulation & Grasping, Dissection, and Thread Manipulation. Each environment is highly parametrizable for increasing difficulty, resulting in a high performance ceiling for new algorithms. We use Proximal Policy Optimization (PPO) to establish a baseline for model-free RL algorithms, investigating the effect of several environment parameters on task difficulty. Finally, we show that many environments and parameter configurations reflect well-known, open problems in RL research, allowing researchers to continue exploring these fundamental problems in a surgical context. We aim to provide a challenging, standard environment suite for further development of RL for RALS, ultimately helping to realize the full potential of cognitive surgical robotics. LapGym is publicly accessible through GitHub (https://github.com/ScheiklP/lap_gym).

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Minimally invasive surgery is the standard for many abdominal interventions, with an increasing use of telemanipulated robots. As collaborative robots enter the field of medical interventions, their intuitive control needs to be addressed. Augmented reality can thereby support a surgeon by representing the surgical scene in a natural way. In this work, an augmented reality based robot control for laparoscopic cholecystectomy is presented. A user can interact with the virtual scene to clip the cystic duct and artery as well as to manipulate the deformable gallbladder. An evaluation was performed based on the SurgTLX and system usability scale.