Cataract is the leading cause of blindness worldwide with an increasing number of patients due to changing demographics, making automation an important part in future surgical treatment. In this work, we focus on a substep of cataract surgery, the Continuous Curvilinear Capsulorhexis (CCC). With a high complexity, this task is an ideal candidate for Reinforcement Learning (RL) in simulation. First, we present an interactive and physically realistic simulation based on the Finite Element Method (FEM) that mimics the tearing behavior of soft tissue during CCC. Then, we train and evaluate RL models in simulation, demonstrating that the trained policies can complete the CCC in 85% of cases. We also show that applying domain randomization techniques make the policy more robust against changes in geometrical and biomechanical boundary conditions.

While robotic arms have been used in a vast range of application areas, so far no extensive reports on the utilization as human-machine interface exist. Compared to HMI devices from literature, the robotic arm used in this work (KUKA LBR iiwa 14 R820) features a relatively large workspace and is able to generate force and torque feedback that surpasses the capabilities of literature devices. We describe the setup allowing to use the robotic arm as HMI and analytically determine the optimal initial pose of it based on the manipulability measure of Yoshikawa. To demonstrate that the robotic arm is able to serve as HMI, we report on a comparative study with a state of the art haptic HMI featuring 20 participants. Additionally, two applications from the context of planetary exploration are presented: The first considers the teleoperation of the pan-tilt unit of a lightweight rover unit and illustrates how the large workspace of the HMI benefits the precision of the teleoperation compared to a setup with a smaller workspace. The second experiment showcases the use of the force feedback of the HMI to enable a cooperation between the operator and a supporting path-following automation in a shared control of a simulated ground robot. Both the study and the applications highlight the performance, precision and reliability of our proposed system.