Reproducing Expert-Like Motion in Deformable Environments Using Active Learning and IOC

We propose a method that allows a motion planning algorithm to imitate the behavior of expert users in deformable environments. For instance, a surgeon inserting a probe knows intuitively which organs are more sensitive than others, but may not be able to mathematically represent a cost function that governs his or her motion. We hypothesize that the relative sensitivities of deformable objects are encoded in the expert’s demonstrated motion and present a framework which is able to imitate an expert’s behavior by learning a sensitivity-based cost function under which the expert’s motion is optimal. Our framework consists of three stages: (1) Automatically generating demonstration tasks that prompt the user to provide informative demonstrations through an active learning process; (2) Recovering object sensitivity values using an Inverse Optimal Control technique; and (3) Reproducing the demonstrated behavior using an optimal motion planner. We have tested our framework with a set of 5DoF simulated and 3DoF physical test environments, and demonstrate that it recovers object parameters suitable for planning paths that imitate the behavior of expert demonstrations. Additionally, we show that our method is able to generalize to new tasks; e.g. when a new obstacle is introduced into the environment.