Surfing on an uncertain edge: Precision cutting of soft tissue using torque-based medium classification (1909.07247v1)

Abstract: 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.

• The paper introduces a torque-based classifier using logistic regression for medium differentiation, improving robotic precision in cutting tasks.
• It employs Dynamic Movement Primitives from human demonstrations to develop a nominal trajectory for adaptive control in soft tissue environments.
• Quantitative tests show a 72% success rate in boundary following with closed-loop control, marking a significant improvement over open-loop methods.

Insights into Torque-Based Medium Classification for Soft-Tissue Cutting

The paper "Surfing on an Uncertain Edge: Precision Cutting of Soft Tissue Using Torque-Based Medium Classification" introduces an innovative approach to precision cutting tasks in robotics, particularly focusing on situations characterized by uncertainty in medium boundaries. The authors propose a torque-based mechanism for medium classification in soft tissue environments to guide robotic manipulation in tasks where visibility is constrained, such as surgical procedures and precision food processing.

The core contribution of this work is the development of a control strategy that utilizes torque measurements to infer the boundary between two soft mediums. The primary test application selected by the researchers is the extraction of grapefruit segments, a problem bearing substantial resemblance to surgical excision tasks. The task involves navigating the boundary between the grapefruit peel and pulp to successfully extract a segment without damaging it or embedding the knife into the peel.

Methodological Approach

The proposed methodology hinges on three pivotal components. First, a binary medium classifier is developed using logistic regression based on joint torque readings. This classifier is capable of distinguishing between different mediums (e.g., peel and pulp) within the task space. The decision boundary within the classifier informs the desired cutting path, positing higher uncertainty values at the boundary's location.

Second, the Dynamic Movement Primitives (DMP) framework is employed to learn a nominal trajectory for scooping motion, derived from human demonstrations. The DMP serves as a baseline, capturing a generalized movement pattern for the task but lacks adaptability to all task variations.

Finally, a closed-loop control is implemented, wherein the probability distributions over the mediums guide real-time trajectory modifications. This control scheme responds to the uncertainty feedback from the torque-based classifier, dynamically steering the robot's end-effector along the estimated boundary of mediums.

Quantitative Evaluation

The effectiveness of the approach was empirically validated through numerous trials on grapefruit, with contrastive outputs observed between open-loop and closed-loop control systems. The proposed closed-loop torque feedback mechanism demonstrated a 72% success rate in boundary following and segment extraction, a notable improvement over the 50% success rate recorded with only a nominal trajectory.

Implications and Future Work

This paper lays a foundation for more flexible and informed robotic manipulation strategies in environments where boundaries are uncertain and visually imperceptible. The torque-based medium classification offers a promising dimension to autonomous surgical tasks where tactile feedback can significantly enhance precision and safety. Furthermore, the approach can translate to industries beyond healthcare, such as automated food processing, where dexterous manipulation of delicate materials is often requisite.

Future research could delve into refining the adaptive control schemes and exploring robust movement adaptation strategies that dynamically respond to variable boundary conditions. Additionally, integrating such a torque-based approach with vision-based systems or refined material property analysis could yield more comprehensive solutions for complex manipulation tasks.

In conclusion, this paper presents a significant advancement in bridging the gap between soft-tissue manipulation under uncertainty and real-world applicability, driving the field of robotic autonomy towards more intelligent and adaptive systems.