Intuitive teaching of medical device operation to clinical assistance robots

The adjustment of medical devices in the operating room is currently done by the circulating nurses. As digital interfaces for the devices are not foreseeable in the near future and to incorporate legacy devices, the robotic operation of medical devices is an open topic.

We propose a teleoperated learning from demonstration process to acquire the high-level device functionality with given motion primitives. The proposed system is validated using an insufflator as an exemplary medical device.

At the beginning of the proposed learning period, the teacher annotates the user interface to obtain the outline of the medical device. During the demonstrated interactions, the system observes the state change of the device to generalize logical rules describing the internal functionality. The combination of the internal logics with the interface annotations enable the robotic system to adjust the medical device autonomously. To interact with the device, a robotic manipulator with a finger-like end-effector is used while relying on haptic feedback from torque sensors.

The proposed approach is a first step towards teaching a robotic system to operate medical devices. We aim at validating the system in an extensive user study with clinical personnel. The logical rule generalization and the logical rule inference based on computer vision methods will be focused in the future.