Ensuring the sterility of medical equipment, particularly endoscopes used in environments teeming with diverse pathogens and drug-resistant bacteria, is crucial for safe medical procedures. However, the complexity of endoscope reprocessing, which involves numerous dexterous manual manipulations, poses significant challenges. Achieving certification for sterilization requires precise, repetitive execution with strict tolerances. In this study, we propose a framework that automates the handling and storage of endoscopes right after the sterilization process and employs compliant collaborative robots to address these dexterous manipulation challenges. In the first stage, we identified the key manipulation skills involved in the process through observations and feedback from medical personnel. In the second stage, we proposed a system that employs a high-level action planner to orchestrate the removal and storage of endoscopes, integrating two collaborative robots and a linear unit. Through real-time force measurements, compliant control, task knowledge, and safety protocols, we establish a system that ensures the safety of both medical equipment and personnel in proximity. In our first experiment, we conducted 50 trials with a 100 % reliability rate. Each trial had an execution time of 102 seconds, with a variance of 1.2 seconds. In our second experiment, we performed 10 trials with a human obstructing the transfer path, facing away from the robot. In all cases, the system successfully and promptly detected the collision. This work pioneers the automation of medical reprocessing in sterile environments using tactile robots and addresses the associated challenges.

In this work, we advance robotic grasping by incorporating wrist compliance in a unified hand-arm system inspired by human limb coordination. This integration improves grasping reliability and robustness through impedance and force learning in robotic arms. The compliant wrist system effectively compensates for uncertainties in object position and orientation. Employing a combined impedance-force control approach, we address diverse grasping and manipulation tasks in simulation. Successfully transferring the learned policy to a service humanoid mobile robot enables the seamless execution of grasping and opening tasks for various doors and handles without additional learning, using both fully actuated and underactuated robotic hands. Remarkably, our robust strategies yielded only one failure in 30 trials for the underactuated hand, even with up to 8 cm translation normal to the handle and$33^\circ$rotation errors, and no failures for the fully actuated one with up to 12 cm translation and$30^\circ$rotation. This significantly outperforms state-of-the-art end-to-end reinforcement learning approaches. Furthermore, we successfully tested and validated our approach across various constrained everyday tasks in different environments. Our proposed framework represents an advancement in the learning and execution of power grasping with compliant manipulation, achieving practically relevant performance.

Sophisticated manipulation requires both compliance and accuracy. While tactile robots excel at being compliant, their accuracy is often inadequate for complex manipulation. Contact-rich assembly tasks, such as the insertion and manipulation of objects with small tolerances pose an enormous challenge. Complex, highly integrated assemblies, especially in high-tech areas such as robotics, sensors, or machines, still require human personnel, as they cannot be automated in a satisfactory way. To automate such tasks, especially in the context of labor shortage and Industry 4.0, these limitations must be overcome. Robots need to guarantee force limits for active environments in order to avoid harm or damage. Therefore, in this work, we adapt standard Cartesian impedance control by introducing an integration term for position accuracy and wrench limits for safe compliant interaction with unknown and active environments. We combine this with a virtual energy tank to guarantee the general passivity of the controller. Our controller is benchmarked against standard impedance control for absolute positioning accuracy across the robot workspace. Furthermore, we show its applicability to an industrial insertion task. We demonstrate absolute positioning accuracy (residual error| Ax| < 4e – 4) comparable to rigid robots while preserving compliant behavior.