Surfing on an uncertain edge: Precision cutting of soft tissue using torque-based medium classification
This addresses a challenging robotics problem for soft-tissue manipulation, but it is incremental as it builds on existing methods like dynamic movement primitives.
The paper tackles the problem of precision cutting along the boundary between two soft mediums, such as grapefruit pulp and peel, by using a torque-based binary classifier and closed-loop control, achieving a success rate of 72% compared to 50% with a nominal trajectory.
Precision cutting of soft-tissue remains a challenging problem in robotics, due to the complex and unpredictable mechanical behaviour of tissue under manipulation. Here, we consider the challenge of cutting along the boundary between two soft mediums, a problem that is made extremely difficult due to visibility constraints, which means that the precise location of the cutting trajectory is typically unknown. This paper introduces a novel strategy to address this task, using a binary medium classifier trained using joint torque measurements, and a closed loop control law that relies on an error signal compactly encoded in the decision boundary of the classifier. We illustrate this on a grapefruit cutting task, successfully modulating a nominal trajectory fit using dynamic movement primitives to follow the boundary between grapefruit pulp and peel using torque based medium classification. Results show that this control strategy is successful in 72 % of attempts in contrast to control using a nominal trajectory, which only succeeds in 50 % of attempts.