Learning human-like skills for cutting soft objects using force sensing

Abstract

This thesis investigates the application of force sensing to learn robotic cutting of soft objects.

The automation of deformable object cutting is a promising prospect for many important areas, ranging from the food processing industry to soft tissue surgery. However, the remarkable robustness with which humans perform these tasks is far beyond the capabilities of current robotics. Humans achieve this robustness by employing various cutting strategies that rely on tactile feedback. This thesis investigates these abilities, ways of sensing and modeling these, and approaches to exploit these for robotic cutting, through four key research contributions.

The first formulates and confirms the hypothesis that forces play a key role in the robustness of cutting skills. This study investigated the human skills of scooping a grapefruit with a knife. The insight behind the hypothesis is that humans guide the knife’s movement using tactile cues that arise at the pulp/peel interface. Experiments conducted in this thesis indicate that similar torque-based movement adaptation is an effective strategy in robotic grapefruit scooping. The proposed method can be used in many practical applications where cutting along the medium boundary is required; for example, in surgical excision of solid tumours within soft tissue.

A second study considered the practical implementation of robotic cutting systems that must account for a number of constraints. In many cutting tasks, the required adaptation of cutting movement is subject to a non-holonomic constraint that restricts the lateral motion of the blade. This makes it difficult to encode cutting motions using dynamical system-based methods, such as dynamical movement primitives (DMPs), otherwise well suited for learning complex reactive behaviours. The non-holonomic DMPs proposed in this thesis introduce a coupling term derived by the Udwadia- Kalaba method that guarantees run-time satisfaction of a wide range of constraints, including non-holonomic. We demonstrate how this approach can be applied to learn robotic cutting skills from demonstration.

A third study on the role of forces in surgical excisions has shown that the force modality contains valuable information for skill understanding. It was found that incision forces consist of subject-specific signatures that reflect excision assessment by experts. We proposed a generative model of excision forces, which decomposes cutting behaviour into amplitude and temporal components that encode meaningful characteristics of the observed behaviour. Along with a novel sensorised instrument developed for this study, this model can form the basis for surgical training systems with objective skill assessment and opens up many opportunities for learning humanlike robotic excision of soft tissues.

Finally, these approaches were combined for learning human-like robotic elliptical excision skills, following an approach using the previously developed sensorised instrument and the model of elliptical excision forces.We introduced a generative model for pose trajectories of the blade in the elliptical excision task and used it to encode the observed excision behaviours. We demonstrate how the proposed model of excision forces can be employed to optimise the robotic behaviour with respect to the performance assessment of experts and the desired human-like characteristics of cutting forces.

This work let us analyse complex cutting tasks, techniques and skills from human demonstrations. Such analysis can lead us to understand better what underlies these skills in humans and how these can be replicated by a robot.