An average person with type 1 diabetes and no insulin pump sticks a needle into their abdomen between 700 and 1,000 times per year. A person with the hormone disorder acromegaly travels to a doctor's office to receive a painful injection into the muscles of the butt once a month. Someone with multiple sclerosis may inject the disease-slowing interferon beta drug three times per week, varying the injection site among the arms, legs and back. Medical inventor Mir Imran, holder of more than 400 patents, spent the last seven years working on an alternate way to deliver large drug molecules like these, and his solution--an unusual "robotic" pill--was recently tested in humans. The RaniPill capsule works like a miniature Rube Goldberg device: Once swallowed, the capsule travels to the intestines where the shell dissolves to mix two chemicals to inflate a balloon to push out a needle to pierce the intestinal wall to deliver a drug into the bloodstream.

Surgeons normally need surgical scissors and tissue grippers to cut through a deformable surgical tissue. The cutting accuracy depends on the skills to manipulate these two tools. Such skills are part of basic surgical skills training as in the Fundamentals of Laparoscopic Surgery. The gripper is used to pinch a point on the surgical sheet and pull the tissue to a certain direction to maintain the tension while the scissors cut through a trajectory. As the surgical materials are deformable, it requires a comprehensive tensioning policy to yield appropriate tensioning direction at each step of the cutting process. Automating a tensioning policy for a given cutting trajectory will support not only the human surgeons but also the surgical robots to improve the cutting accuracy and reliability. This paper presents a multiple pinch point approach to modelling an autonomous tensioning planner based on a deep reinforcement learning algorithm. Experiments on a simulator show that the proposed method is superior to existing methods in terms of both performance and robustness.