Computer Vision News - December 2021

32 Exclusive Interview How can we achieve this? This iswhere themachine learningcomes in, and just generally the much greater power of computers, making it possible to have much richer environmental models for this situational awareness. There is a danger with that as well that machine learning methods need to learn more humility. They do not know what they do not know. One of the challenges is, how can the machine recognize when it is in a situation it has not been trained for? We are beginning to see some research in that area, but I think it is a key barrier. What can you see happening next? I see there being a gradual increase in the capabilities of these systems either remotely, like teleoperated systems, or another way of controlling these robots which is more like hand-over-hand control. If the robot and the surgeon are holding a tool and the surgeon pushes on the tool, the robot can feel that and move accordingly. But because it is a robot, and it does not have hand tremor it can also refuse to move in a certain direction. It can enforce a safety barrier, or control can be shared in various other ways between the surgeon and the robot. In some cases, the robot might be adding a palpating motion to some guidance a surgeon is using, say with a joystick or hand-over-hand control. In other cases, the surgeon might guide the robot to a place where it can start and take over and do something on its own, which is what happened with the system we developed at IBM all those years ago. The surgeon used hand-over-hand guiding to help get the robot into the position where first it could do some registration, but then they would hand-over-hand guide it to a position where it was safe for the robot to begin machining, and then it would take over and do that task on its own while the surgeon supervised. I think you are going to see a lot of examples Copyright Russell Taylor - used with permission