Managing indirect access in laparoscopy as a minimally invasive procedure poses challenges to physicians. In particular, an endoscope must be navigated to achieve adequate visualization of the surgical anatomy, while coping with unergonomic poses, tremor, and fatigue. Furthermore, the alignment of visual perception and physical movement, dictated by the endoscope's position relative to the monitor, can lead to hand-eye coordination challenges. We propose unified deployment of a robotic endoscope holder together with an augmented reality display to counteract the aforementioned challenges in laparoscopy. Our augmented reality system provides an interactive, stereoscopic, virtual monitor displaying an endoscopic stream. In addition, our method design enables direct control of the robotic endoscope holder. Our user study demonstrates the potential of the proposed method to significantly improve hand-eye coordination, while insights from our usability study for robotic control indicate promising trends, including high usability and low cognitive demand.

In this work, we present a learning from demonstration solution for automating a surgical blunt dissection task. In addition to learning motion trajectories, our goal is to learn variable impedance behaviors that enable the robot to interact safely and compliantly during the task. To that end, we propose a teaching interface using bilateral teleoperation, which allows the natural transfer of human motions and impedance behaviors skills to robots. The demonstrated profiles are captured with Dynamic Movement Primitives and Gaussian Mixture Models, which subsequently provide the robot a reference motion plan, and a stiffness adaptation policy, during physical interaction. Experimental validation on real robot hardware shows the effectiveness of the proposed approach in terms of ensuring successful task execution, as well as safety compared to stiff high-gain control.

Safety is one of the key issues in the use of robots, especially when human-robot interaction is targeted. Although unforeseen environment situations, such as collisions or unexpected user interaction, can be handled with specially tailored control algorithms, hard- or software failures typically lead to situations where too large torques are controlled, which cause an emergency state: hitting an end stop, exceeding a torque, and so on-which often halts the robot when it is too late. No sufficiently fast and reliable methods exist which can early detect faults in the abundance of sensor and controller data. This is especially difficult since, in most cases, no anomaly data are available. In this paper we introduce a new robot anomaly detection system (RADS) which can cope with abundant data in which no or very little anomaly information is present.

This paper presents a method to calibrate the model of serial and flexible lightweight robots with joint sided torque sensors in the assembled state. The calibration is done in an iterative three-step process, based on static robot poses. In the first step the kinematics and stiffnesses of the flexible components are calibrated. Second the models of the integrated torque sensors are identified in a linear least square solution. In the third step the masses, the centers of gravity and the torque sensor offsets are estimated using linear regression. The calibration steps are repeated stepwise to account for their dependencies. The calibration procedure is simulated and experimentally performed with the medical lightweight robot MIRO of the German Aerospace Center. Through the iterative procedure the pose accuracy improves from about 5mm translational error and 2.5 ° rotational error to 1mm and 0.3 ° regarding the entire workspace.