Service robots are becoming a common sight in hospital corridors in many developed countries, but their acceptance, the factors influencing it, and potential cultural differences between countries remain unclear. To investigate this, the authors examined the use of service robots in two hospitals, one in Denmark and the other in South Korea, through an exploratory study. The analysis was performed based on retrospective data including usage parameters and a survey was conducted to assess the attitudes of healthcare personnel toward blood sample and medication delivery robots. The survey found that the staff in both hospitals were positive regarding the deployment of such robots. Additionally, the results indicate that the individualization of robots may lead to higher agency and that swift removal of technical issues at deployment might positively influence longer-term satisfaction with the robots.

Femoral artery access is a common and critical procedure for various cardiovascular interventions. Although it is a time critical operation, accessing the Common Femoral Artery (CFA) typically requires expertise found in specialized medical settings. The necessity for specialized personnel or transport to equipped facilities can lead to delays, potentially exacerbating patient outcomes. To address this challenge, a portable and cost-effective robotic device that autonomously localizes a CFA and precisely positions a needle guide is developed. Through the needle guide, needle can be quickly and accurately inserted into the artery even by non-specialist physicians. Different from the conventional B-mode ultrasound guided procedure, the proposed robotic solution utilizes a Doppler transducer for detecting the arterial location and employs a single M-mode transducer for depth measurement. A series of experiments are designed and conducted to validate the system’s feasibility, achieving high accuracy within 2 mm, rapid processing within 1.5 min, and a 100% success rate, thus proving the system’s efficacy. These results convince us for further refinement of the system and support its evaluation in animal studies.

We present a novel application of Learning from Demonstration to realize a fully autonomous bi-manual surgical suturing task, including needle pick up, insertion, re-grasping, extraction and hand-over. Surgical action primitives are learned from a single human demonstration and encoded into an action library from which they are pulled to compose more elaborate tasks at planning/execution time. The method is demonstrated in a non-clinical setting, using unmodified surgical instruments with a custom surgical robot system. We use stereo vision to automatically detect the suture needle and entry points to close the control loop and generalize tasks to different task conditions. The suturing task is shown to generalize well to differing initial conditions with a success rate of 17 % for the full task, a mean subtask success rate of 75 % and mean needle insertion error of 3.3 mm over the course of 46 trial task executions at human speed. Failures could all be attributed to erroneous vision-based detection, pose estimation and robot calibration.

Vision systems are often used jointly with robotic manipulators to perform automated tasks in industrial applications. Still, the correct set up of such workcells is difficult and requires significant resources. One of the main challenges, when implementing such systems in industrial use cases, is the pose uncertainties presented by the vision system which have to be handled by grasping. In this paper, we present a framework for the design and analysis of optimal gripper finger designs and vision parameters. The proposed framework consists of two parallel methods which rely on vision and grasping simulation to provide an initial estimation of the uncertainty compensation capabilities of the designs. In case the compensation is not feasible with the initial design, an optimization process is introduced, to select the optimal pose estimation parameters and finger designs for the presented task. The proposed framework was evaluated in dynamic simulation and implemented in a real industrial use case.

In this paper we present an initial approach towards reversible robot movement primitives. Our approach is a modification of Dynamic Movement Primitives (DMPs), a widely used framework for robot learning from demonstration. DMPs are based on dynamical systems to guarantee properties such as convergence to a goal state, robustness to perturbation, and the ability to generalize to other goal states. Yet a main limitation of their original formulation is that they do not allow for movements to be reversed. Thus, to execute the same task forwards and backwards would mean to learn two separate primitives. We propose to replace the transformation system in DMPs with the Logistic Differential Equation (LDE), a known time-reversible non-linear system. Similarly to the original DMP formulation, our system's temporal evolution is controlled by a phase system, which in our case is derived from the LDE to guarantee reversibility. We evaluate our approach experimentally with demonstration data from a real robot assembly task, and show comparable properties to those of the original DMP system.

We present a three-level cognitive system in a learning by demonstration context. The system allows for learning and transfer on the sensorimotor level as well as the planning level. The fundamentally different data structures associated with these two levels are connected by an efficient mid-level representation based on so-called “semantic event chains.” We describe details of the representations and quantify the effect of the associated learning procedures for each level under different amounts of noise. Moreover, we demonstrate the performance of the overall system by three demonstrations that have been performed at a project review. The described system has a technical readiness level (TRL) of 4, which in an ongoing follow-up project will be raised to TRL 6.

Optical coherence tomography (OCT) is an interferometric imaging modality with spatial resolution in the micrometer range. The OCT signal can be used to detect small structures to measure deformation or to characterize tissue. Moreover, OCT can be realized through a single optical fiber, i.e., it can be easily integrated with instruments. However, to use OCT for intra-operative guidance its spatial alignment needs to be established. Hence, we consider eye-in-hand calibration between the 1D OCT imaging and a 6D robotic position system. We present a method to perform pivot calibration for OCT and based on this introduce pivot+d, a new 1D to 6D eye-in-hand calibration. We provide detailed results on the convergence and accuracy of our method and use translational and rotational ground truth to show that our methods allow for submillimeter positioning accuracy of an OCT beam with a robot. For pivot calibration we observe a mean translational error of 0.5161 ± 0.4549 mm while pivot+d shows 0.3772 ± 0.2383 mm. Additionally, pivot+d improves rotation detection by about 8° when compared to pivot calibration.

Programming by Demonstration (PbD) is an efficient way for non-experts to teach new skills to a robot. PbD can be carried out in different ways, for instance, by kinesthetic guidance, teleoperation or by using external controls. In this paper, we compare these three ways of controlling a robot in terms of efficiency, effectiveness (success and error rate) and usability. In an industrial assembly scenario, 51 participants carried out peg-in-hole tasks using one of the three control modalities. The results show that kinesthetic guidance produces the best results. In order to test whether the problems during teleoperation are due to the fact that users cannot, like in kinesthetic guidance, switch between control points using traditional teleoperation devices, we designed a new device that allows users to switch between controls for large and small movements. A user study with 15 participants shows that the novel teleoperation device yields almost as good results as kinesthetic guidance.

In this paper, the causes for singularity of a robot arm in teleoperation for robot learning from demonstration are analyzed. Singularity is the alignment of robot joints, which prevents the configuration of the inverse kinematics. Inspired by users' own hypotheses, we investigated speed and delay as possible causes. The results show that delay causes problems during teleoperation though not in direct control with a control panel because users expect a different, more intuitive control in teleoperation. Speed on the other hand was not found to have an effect on the occurrence of singularity.

In this paper, we demonstrate how a generic industrial robot can be used as a training partner, for upper limb training. The motion path and human/robot interaction of a non-generic upper-arm training robot is transferred to a generic industrial robot arm, and we demonstrate that the robot arm can implement the same type of interaction, but can expand the training regime to include both upper arm and shoulder training. We compare the generic robot to two affordable but custom-built training robots, and outline interesting directions for future work based on these training robots.

Commercial telerobotic surgery systems for soft tissue surgery are generally limited to visual imaging, though it is possible to simultaneously view picture-in-picture visualization of another workstation. However, it is not easy to correlate such information with the primary endoscopic view since it may not relate to the surface visible in the visual endoscopic images. As critical surfaces as well as surgical targets often lie subsurface, a range of techniques (e.g. ultrasound and near-infrared imaging) and registration methods have been investigated as robotic surgery gains popularity. While investigation of nerves, blood vessels, and tumors has received prior attention, we present a new prototype system for real-time multimodal image registration that focuses on the visualization of the urinary tract. By providing an accurate registration between stereo video images and a near infrared imager, we aim to enhance surgical awareness and make critical uretary tasks such as mobilization of the ureters easier. Engineering validation experiments with a prototype imager, and in-vivo experiments using a prototype Hamamatsu Photodynamic Eye (PDE) imager together with the da Vinci surgical system demonstrating feasibility are presented. These initial experiments have also shown encouraging response from the clinicians.