Deep Homography Prediction for Endoscopic Camera Motion Imitation Learning

In this work, we investigate laparoscopic camera motion automation through imitation learning from retrospective videos of laparoscopic interventions. A novel method is introduced that learns to augment a surgeon's behavior in image space through object motion invariant image registration via homographies. Contrary to existing approaches, no geometric assumptions are made and no depth information is necessary, enabling immediate translation to a robotic setup. Deviating from the dominant approach in the literature which consist of following a surgical tool, we do not handcraft the objective and no priors are imposed on the surgical scene, allowing the method to discover unbiased policies. In this new research field, significant improvements are demonstrated over two baselines on the Cholec80 and HeiChole datasets, showcasing an improvement of 47% over camera motion continuation. The method is further shown to indeed predict camera motion correctly on the public motion classification labels of the AutoLaparo dataset. All code is made accessible on GitHub.Comment: Early accepted at MICCAI 202

Sim2Real Transfer of Reinforcement Learning for Concentric Tube Robots

Concentric Tube Robots (CTRs) are promising for minimally invasive interventions due to their miniature diameter, high dexterity, and compliance with soft tissue. CTRs comprise individual pre-curved tubes usually composed of NiTi and are arranged concentrically. As each tube is relatively rotated and translated, the backbone elongates, twists, and bends with a dexterity that is advantageous for confined spaces. Tube interactions, unmodelled phenomena, and inaccurate tube parameter estimation make physical modeling of CTRs challenging, complicating in turn kinematics and control. Deep reinforcement learning (RL) has been investigated as a solution. However, hardware validation has remained a challenge due to differences between the simulation and hardware domains. With simulation-only data, in this work, domain randomization is proposed as a strategy for translation to hardware of a simulation policy with no additionally acquired physical training data. The differences in simulation and hardware forward kinematics accuracy and precision are characterized by errors of 14.74±8.87 mm or 26.61±17.00 % robot length. We showcase that the proposed domain randomization approach reduces errors by 56 % in mean errors as compared to no domain randomization. Furthermore, we demonstrate path following capability in hardware with a line path with resulting errors of 4.37±2.39 mm or 5.61±3.11 % robot length

Concentric-Tube-Eccentric-Rod: A Continuum Actuator for Macro-/Micro-Scale Translational Actuation

This paper proposes a motion de-amplification-basedactuator design, which comprises coarse and fine-stagesthat respectively achieve macro and micro-scale linearactuation. The proposed de-amplification mechanism, whichoutputs micrometer-level translation from scaling downmillimeter-level translation input, is a hybrid continuummechanism formed by embedding a many-backbone continuummechanism in a concentric tube mechanism. A simplifiedCosserat-rod-based analytical model incorporating a lumpedCoulomb friction model is derived to describe themicro-actuation input/output behavior. A prototype isfabricated and its micro-motion capabilities arecharacterized by experiments. The results demonstrate theactuator's micro-motion output possesses a travel stroke of$232.48,$$mu$m, resolution of $0.216,$$mu$m, repeatabilityof $1.92,$$mu$m (standard deviation), and output complianceamong $13.52, - 16.01,$$mu$m/N. Besides, open-looppositioning tests show a steady state error of $1.67,$$mu$m(mean). The actuator exhibits excellent performance and canbe created from off-the-shelf components using conventionalworkshop tools