There has been an increasing demand to automate the non-patient care matters so that the clinical staff can focus on delivering patient care. For example, out-patients undergoing chemotherapy increases their toilet usage frequency due to the treatment. As they are undergoing chemotherapy, their output waste contains a level of chemical. This task is compulsory yet troublesome and time-consuming so it is often desired to be removed from the nursing staff for them to focus on patient care. Hence, in this paper, we propose a manipulator system to automatically dispose the bedpan used by patients undergoing chemotherapy. The main technical challenge lies in the removal of the bedpan from the commode as the interaction of the grasping is highly dynamic, along with the different conditions of the bedpans. To address this manipulation issue, a Residual Reinforcement Learning (RRL) method that leverages vision-based commode pose estimation and the reinforcement learning (RL)-based uncertainty compensation for improvement of the grasping accuracy is proposed to increase the robustness of the disposal. The experiments conducted show that the manipulator can dispose the bedpan without human intervention and the proposed method achieves a 100 % success rate while the traditional method without RL is only 50 %.

Percutaneous Nephrolithotomy is a recommended treatment method for large kidney stone removal. However, the first and most important step, i.e., getting the percutaneous access to create the tract between the targeted calyx and the flank skin, is challenging as the surgeon is often occupied by several tasks at a given time. Therefore, in this paper, we propose a robotic assisted system that collaborates with the surgeon and provides assistance in order for the surgeons to focus on more critical jobs resulting in better surgical performance. A procedure for this robot including three working stages is described. This procedure allows the surgeon to choose a suitable percutaneous target using an ultrasound probe based on his or her experience and the robot will track the respiratory motion of the target kidney stone and insert the needle automatically after the surgeon releases the probe. Experiments are conducted to demonstrate the procedure with the proposed assisted robot for PCNL.