A knowledgebased framework for task automation in surgery Robotic surgery has significantly improved the quality of surgical procedures. In the past, researches have been focused on automating simple surgical actions. However, there exists no scalable framework for automation in surgery. In this paper, we present a knowledgebased modular framework for the automation of articulated surgical tasks, for example, with multiple coordinated actions. The framework is consisted of ontology, providing entities for surgical automation and rules for task planning, and xe2x80x9cdynamic movement primitivesxe2x80x9d as adaptive motion planner as to replicate the dexterity of surgeons. To validate our framework, we chose a paradigmatic scenario of a pegandring task, a standard training exercise for novice surgeons which presents many challenges of real surgery, e.g. grasping and transferring. Experiments show the validity of the framework and its adaptability to faulty events. The modular architecture is expected to generalize to different tasks and platforms.

An Asynchronous MultiBody Simulation Framework for RealTime Dynamics, Haptics and Learning with Application to Surgical Robots Surgical robots for laparoscopy consist of several patient side slave manipulators that are controlled via surgeon operated master telemanipulators. Commercial surgical robots do not perform any subtasks xe2x80x93 even of repetitive or noninvasive nature xe2x80x93 autonomously or provide intelligent assistance. While this is primarily due to safety and regulatory reasons, the state of such automation intelligence also lacks the reliability and robustness for use in highrisk applications. Recent developments in continuous control using Artificial Intelligence and Reinforcement Learning have prompted growing research interest in automating mundane subtasks. To build on this, we present an inspired Asynchronous Framework which incorporates realtime dynamic simulation xe2x80x93 manipulable with the masters of a surgical robot and various other input devices xe2x80x93 and interfaces with learning agents to train and potentially allow for the execution of shared subtasks. The scope of this framework is generic to cater to various surgical (as well as nonsurgical) training and control applications. This scope is demonstrated by examples of multiuser and multimanual applications which allow for realistic interactions by incorporating distributed control, shared task allocation and a welldefined communication pipeline for learning agents. These examples are discussed in conjunction with the design philosophy, specifications, systemarchitecture and metrics of the Asynchronous Framework and the accompanying Simulator. We show the stability of Simulator while achieving realtime dynamic simulation and interfacing with several haptic input devices and a training agent at the same time.