Surgical robots perform millions of delicate operations annually under human control. Now they’re getting ready to operate on their own.

What’s new: Researchers at UC Berkeley, UC San Francisco, and SRI International trained a machine learning system to pilot a da Vinci two-armed surgical robot through a task that tested its dexterity, precision, and speed, The New York Times reported.

How it works: The system learned via imitation learning to lift tiny plastic rings off a pegboard, pass them from one claw to the other, and slide them onto different pegs. The task is a exercise for surgeons learning to perform laparoscopic procedures, in which a camera and other specialized instruments are inserted into the patient’s body through a small incision.

  • The authors trained an ensemble of four convolutional neural networks on 180 RGBD (red, green, blue, plus depth) video clips of human surgeons using the robot to demonstrate an error and how to correct it, as well as information about the robot’s joint positions. The system learned to perform the task, but its precision degraded over time as the cables that control the robot’s limbs stretched, causing the model to miss its targets.
  • To compensate for the gradual loss of precision, the authors trained an LSTM on motion-capture data of the robot’s joint positions as the machine performed random motions autonomously.
  • Together, the two models proved more agile, precise, and rapid on the ring-and-peg test than human surgeons.

Behind the news: AI already assists physicians in a few small but important procedures. For instance, a robotic tool from the Dutch company Microsure, which helps suture tiny incisions on blood vessels, uses AI to stabilize shaking in the operator’s hands.

Why it matters: This is a nice example of an algorithm that handles concept drift in robotic control. A lot of work in model-based reinforcement learning assumes a fixed model. But just as the dynamics of a human arm change as the arm tires — and a surgeon must adapt to control that tiring arm — we want learning algorithms to adapt to gradual changes in the robot’s dynamics.

We’re thinking: We’re looking to AI systems that help optimize nutrition, exercise, and sleep to help steer us clear of AI systems that wield a scalpel!


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