Deep-Learning-Based Control of a Decoupled Two-Segment Continuum Robot for Endoscopic Submucosal Dissection

2026-02-03

AI Summary

This study addresses the complexity of conventional endoscopic submucosal dissection (ESD) and the limited dexterity of existing single-segment robotic instruments by proposing DESectBot, a dual-segment continuum robot featuring a decoupled architecture and integrated surgical forceps to achieve six-degree-of-freedom distal manipulation. For the first time, a gated recurrent unit (GRU)-based deep learning controller is employed to effectively decouple the nonlinear inter-segment coupling inherent in dual-segment continuum robots during ESD tasks. Experimental results demonstrate that the GRU controller outperforms Jacobian inverse, model predictive control (MPC), feedforward neural networks (FNN), and long short-term memory (LSTM) networks in trajectory tracking (position RMSE: 0.81–1.11 mm; orientation error: 2.59°–4.62°) and fixed-point pose control (error: 0.14 mm / 0.72°). The system achieved 100% success in peg-transfer tasks (average time: 11.8 s) and successfully completed ex vivo ESD procedures, including tissue grasping, lifting, and resection.

Technology Category

• Intelligent Robots: Manipulation
• Natural Language Processing: Safety and Robustness
• Machine Learning: Adversarial Learning & Robustness

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

Manual endoscopic submucosal dissection (ESD) is technically demanding, and existing single-segment robotic tools offer limited dexterity. These limitations motivate the development of more advanced solutions. To address this, DESectBot, a novel dual segment continuum robot with a decoupled structure and integrated surgical forceps, enabling 6 degrees of freedom (DoFs) tip dexterity for improved lesion targeting in ESD, was developed in this work. Deep learning controllers based on gated recurrent units (GRUs) for simultaneous tip position and orientation control, effectively handling the nonlinear coupling between continuum segments, were proposed. The GRU controller was benchmarked against Jacobian based inverse kinematics, model predictive control (MPC), a feedforward neural network (FNN), and a long short-term memory (LSTM) network. In nested-rectangle and Lissajous trajectory tracking tasks, the GRU achieved the lowest position/orientation RMSEs: 1.11 mm/ 4.62{\deg} and 0.81 mm/ 2.59{\deg}, respectively. For orientation control at a fixed position (four target poses), the GRU attained a mean RMSE of 0.14 mm and 0.72{\deg}, outperforming all alternatives. In a peg transfer task, the GRU achieved a 100% success rate (120 success/120 attempts) with an average transfer time of 11.8s, the STD significantly outperforms novice-controlled systems. Additionally, an ex vivo ESD demonstration grasping, elevating, and resecting tissue as the scalpel completed the cut confirmed that DESectBot provides sufficient stiffness to divide thick gastric mucosa and an operative workspace adequate for large lesions.These results confirm that GRU-based control significantly enhances precision, reliability, and usability in ESD surgical training scenarios.

Problem

• Endoscopic Submucosal Dissection
• Continuum Robot
• Dexterity
• Robotic Surgery
• Nonlinear Coupling

Innovation

• continuum robot
• deep learning control
• gated recurrent unit (GRU)
• endoscopic submucosal dissection
• decoupled design