Enhancing robotic telesurgery with sensorless haptic feedback.

PURPOSE: This paper evaluates user performance in telesurgical tasks with the da Vinci Research Kit (dVRK), comparing unilateral teleoperation, bilateral teleoperation with force sensors and sensorless force estimation. METHODS: A four-channel teleoperation system with disturbance observers and sensorless force estimation with learning-based dynamic compensation was developed. Palpation experiments were conducted with 12 users who tried to locate tumors hidden in tissue phantoms with their fingers or through handheld or teleoperated laparoscopic instruments with visual, force sensor, or sensorless force estimation feedback. In a peg transfer experiment with 10 users, the contribution of sensorless haptic feedback with/without learning-based dynamic compensation was assessed using NASA TLX surveys, measured free motion speeds and forces, environment interaction forces as well as experiment completion times. RESULTS: The first study showed a 30% increase in accuracy in detecting tumors with sensorless haptic feedback over visual feedback with only a 5-10% drop in accuracy when compared with sensor feedback or direct instrument contact. The second study showed that sensorless feedback can help reduce interaction forces due to incidental contacts by about 3 times compared with unilateral teleoperation. The cost is an increase in free motion forces and physical effort. We show that it is possible to improve this with dynamic compensation. CONCLUSION: We demonstrate the benefits of sensorless haptic feedback in teleoperated surgery systems, especially with dynamic compensation, and that it can improve surgical performance without hardware modifications.

Transfer of learned dynamics between different surgical robots and operative configurations.

PURPOSE: Using the da Vinci Research Kit (dVRK), we propose and experimentally demonstrate transfer learning (Xfer) of dynamics between different configurations and robots distributed around the world. This can extend recent research using neural networks to estimate the dynamics of the patient side manipulator (PSM) to provide accurate external end-effector force estimation, by adapting it to different robots and instruments, and in different configurations, with additional forces applied on the instruments as they pass through the trocar. METHODS: The goal of the learned models is to predict internal joint torques during robot motion. First, exhaustive training is performed during free-space (FS) motion, using several configurations to include gravity effects. Second, to adapt to different setups, a limited amount of training data is collected and then the neural network is updated through Xfer. RESULTS: Xfer can adapt a FS network trained on one robot, in one configuration, with a particular instrument, to provide comparable joint torque estimation for a different robot, in a different configuration, using a different instrument, and inserted through a trocar. The robustness of this approach is demonstrated with multiple PSMs (sampled from the dVRK community), instruments, configurations and trocar ports. CONCLUSION: Xfer provides significant improvements in prediction errors without the need for complete training from scratch and is robust over a wide range of robots, kinematic configurations, surgical instruments, and patient-specific setups.