Virtual-Work Based Shape-Force Sensing for Continuum Instruments with Tension-Feedback Actuation

Continuum instruments are integral to robot-assisted minimally invasive surgery (MIS), with tendon-driven mechanisms being the most common. Real-time tension feedback is crucial for precise articulation but remains a challenge in compact actuation unit designs. Additionally, accurate shape and external force sensing of continuum instruments are essential for advanced control and manipulation. This paper presents a compact and modular actuation unit that integrates a torque cell directly into the pulley module to provide real-time tension feedback. Building on this unit, we propose a novel shape-force sensing framework that incorporates polynomial curvature kinematics to accurately model non-constant curvature. The framework combines pose sensor measurements at the instrument tip and actuation tension feedback at the developed actuation unit. Experimental results demonstrate the improved performance of the proposed shape-force sensing framework in terms of shape reconstruction accuracy and force estimation reliability compared to conventional constant-curvature methods.

Multi-armed Bandit and Backbone boost Lin-Kernighan-Helsgaun Algorithm for the Traveling Salesman Problems

The Lin-Kernighan-Helsguan (LKH) heuristic is a classic local search algorithm for the Traveling Salesman Problem (TSP). LKH introduces an $\alpha$-value to replace the traditional distance metric for evaluating the edge quality, which leads to a significant improvement. However, we observe that the $\alpha$-value does not make full use of the historical information during the search, and single guiding information often makes LKH hard to escape from some local optima. To address the above issues, we propose a novel way to extract backbone information during the TSP local search process, which is dynamic and can be updated once a local optimal solution is found. We further propose to combine backbone information, $\alpha$-value, and distance to evaluate the edge quality so as to guide the search. Moreover, we abstract their different combinations to arms in a multi-armed bandit (MAB) and use an MAB model to help the algorithm select an appropriate evaluation metric dynamically. Both the backbone information and MAB can provide diverse guiding information and learn from the search history to suggest the best metric. We apply our methods to LKH and LKH-3, which is an extension version of LKH that can be used to solve about 40 variant problems of TSP and Vehicle Routing Problem (VRP). Extensive experiments show the excellent performance and generalization capability of our proposed method, significantly improving LKH for TSP and LKH-3 for two representative TSP and VRP variants, the Colored TSP (CTSP) and Capacitated VRP with Time Windows (CVRPTW).

Teleoperation of Continuum Instruments: Investigation of Linear vs. Angular Commands through Task-Priority Analysis

This paper addresses the challenge of teleoperating continuum instruments for minimally invasive surgery (MIS). We develop and adopt a novel task-priority-based kinematic formulation to quantitatively investigate teleoperation commands for continuum instruments under remote center of motion (RCM) constraints. Using redundancy resolution methods, we investigate the kinematic performance during teleoperation, comparing linear and angular commands within a task-priority scheme. For experimental validation, an instrument module (IM) was designed and integrated with a 7-DoF manipulator. Assessments, simulations, and experimental validations demonstrated the effectiveness of the proposed framework. The experiments involved several tasks: trajectory tracking of the IM tip along multiple paths with varying priorities for linear and angular teleoperation commands, pushing a ball along predefined paths on a silicon board, following a pattern on a pegboard, and guiding the continuum tip through rings on a ring board using a standard surgical kit.

Global Challenge for Safe and Secure LLMs Track 1

This paper introduces the Global Challenge for Safe and Secure Large Language Models (LLMs), a pioneering initiative organized by AI Singapore (AISG) and the CyberSG R&D Programme Office (CRPO) to foster the development of advanced defense mechanisms against automated jailbreaking attacks. With the increasing integration of LLMs in critical sectors such as healthcare, finance, and public administration, ensuring these models are resilient to adversarial attacks is vital for preventing misuse and upholding ethical standards. This competition focused on two distinct tracks designed to evaluate and enhance the robustness of LLM security frameworks. Track 1 tasked participants with developing automated methods to probe LLM vulnerabilities by eliciting undesirable responses, effectively testing the limits of existing safety protocols within LLMs. Participants were challenged to devise techniques that could bypass content safeguards across a diverse array of scenarios, from offensive language to misinformation and illegal activities. Through this process, Track 1 aimed to deepen the understanding of LLM vulnerabilities and provide insights for creating more resilient models.

LoD-Loc: Aerial Visual Localization using LoD 3D Map with Neural Wireframe Alignment

We propose a new method named LoD-Loc for visual localization in the air. Unlike existing localization algorithms, LoD-Loc does not rely on complex 3D representations and can estimate the pose of an Unmanned Aerial Vehicle (UAV) using a Level-of-Detail (LoD) 3D map. LoD-Loc mainly achieves this goal by aligning the wireframe derived from the LoD projected model with that predicted by the neural network. Specifically, given a coarse pose provided by the UAV sensor, LoD-Loc hierarchically builds a cost volume for uniformly sampled pose hypotheses to describe pose probability distribution and select a pose with maximum probability. Each cost within this volume measures the degree of line alignment between projected and predicted wireframes. LoD-Loc also devises a 6-DoF pose optimization algorithm to refine the previous result with a differentiable Gaussian-Newton method. As no public dataset exists for the studied problem, we collect two datasets with map levels of LoD3.0 and LoD2.0, along with real RGB queries and ground-truth pose annotations. We benchmark our method and demonstrate that LoD-Loc achieves excellent performance, even surpassing current state-of-the-art methods that use textured 3D models for localization. The code and dataset are available at https://victorzoo.github.io/LoD-Loc.github.io/.

Data Quality Monitoring through Transfer Learning on Anomaly Detection for the Hadron Calorimeters

The proliferation of sensors brings an immense volume of spatio-temporal (ST) data in many domains for various purposes, including monitoring, diagnostics, and prognostics applications. Data curation is a time-consuming process for a large volume of data, making it challenging and expensive to deploy data analytics platforms in new environments. Transfer learning (TL) mechanisms promise to mitigate data sparsity and model complexity by utilizing pre-trained models for a new task. Despite the triumph of TL in fields like computer vision and natural language processing, efforts on complex ST models for anomaly detection (AD) applications are limited. In this study, we present the potential of TL within the context of AD for the Hadron Calorimeter of the Compact Muon Solenoid experiment at CERN. We have transferred the ST AD models trained on data collected from one part of a calorimeter to another. We have investigated different configurations of TL on semi-supervised autoencoders of the ST AD models -- transferring convolutional, graph, and recurrent neural networks of both the encoder and decoder networks. The experiment results demonstrate that TL effectively enhances the model learning accuracy on a target subdetector. The TL achieves promising data reconstruction and AD performance while substantially reducing the trainable parameters of the AD models. It also improves robustness against anomaly contamination in the training data sets of the semi-supervised AD models.

Teleoperation in Robot-assisted MIS with Adaptive RCM via Admittance Control

This paper presents the development and assessment of a teleoperation framework for robot-assisted minimally invasive surgery (MIS). The framework leverages our novel integration of an adaptive remote center of motion (RCM) using admittance control. This framework operates within a redundancy resolution method specifically designed for the RCM constraint. We introduce a compact, low-cost, and modular custom-designed instrument module (IM) that ensures integration with the manipulator, featuring a force-torque sensor, a surgical instrument, and an actuation unit for driving the surgical instrument. The paper details the complete teleoperation framework, including the telemanipulation trajectory mapping, kinematic modelling, control strategy, and the integrated admittance controller. Finally, the system capability to perform various surgical tasks was demonstrated, including passing a thread through the rings, picking and placing objects, and trajectory tracking.

Admittance Control for Adaptive Remote Center of Motion in Robotic Laparoscopic Surgery

In laparoscopic robot-assisted minimally invasive surgery, the kinematic control of the robot is subject to the remote center of motion (RCM) constraint at the port of entry (e.g., trocar) into the patient's body. During surgery, after the instrument is inserted through the trocar, intrinsic physiological movements such as the patient's heartbeat, breathing process, and/or other purposeful body repositioning may deviate the position of the port of entry. This can cause a conflict between the registered RCM and the moved port of entry. To mitigate this conflict, we seek to utilize the interaction forces at the RCM. We develop a novel framework that integrates admittance control into a redundancy resolution method for the RCM kinematic constraint. Using the force/torque sensory feedback at the base of the instrument driving mechanism (IDM), the proposed framework estimates the forces at RCM, rejects forces applied on other locations along the instrument, and uses them in the admittance controller. In this paper, we report analysis from kinematic simulations to validate the proposed framework. In addition, a hardware platform has been completed, and future work is planned for experimental validation.

Hybrid Force Motion Control with Estimated Surface Normal for Manufacturing Applications

This paper proposes a hybrid force-motion framework that utilizes real-time surface normal updates. The surface normal is estimated via a novel method that leverages force sensing measurements and velocity commands to compensate the friction bias. This approach is critical for robust execution of precision force-controlled tasks in manufacturing, such as thermoplastic tape replacement that traces surfaces or paths on a workpiece subject to uncertainties deviated from the model. We formulated the proposed method and implemented the framework in ROS2 environment. The approach was validated using kinematic simulations and a hardware platform. Specifically, we demonstrated the approach on a 7-DoF manipulator equipped with a force/torque sensor at the end-effector.

A Bimanual Teleoperation Framework for Light Duty Underwater Vehicle-Manipulator Systems

In an effort to lower the barrier to entry in underwater manipulation, this paper presents an open-source, user-friendly framework for bimanual teleoperation of a light-duty underwater vehicle-manipulator system (UVMS). This framework allows for the control of the vehicle along with two manipulators and their end-effectors using two low-cost haptic devices. The UVMS kinematics are derived in order to create an independent resolved motion rate controller for each manipulator, which optimally controls the joint positions to achieve a desired end-effector pose. This desired pose is computed in real-time using a teleoperation controller developed to process the dual haptic device input from the user. A physics-based simulation environment is used to implement this framework for two example tasks as well as provide data for error analysis of user commands. The first task illustrates the functionality of the framework through motion control of the vehicle and manipulators using only the haptic devices. The second task is to grasp an object using both manipulators simultaneously, demonstrating precision and coordination using the framework. The framework code is available at https://github.com/stevens-armlab/uvms_bimanual_sim.