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International Symposium on Medical robotics (ISMR)

作者： Paramjit Singh Bawejaab Radian Gondokaryonobcd Lueder A. Kahrs Manipal Institute of Technology Karnataka India Medical Computer Vision and Robotics Lab University of Toronto Canada Department of Computer Science University of Toronto Canada The Wilfred and Joyce Posluns CIGITI SickKids Toronto Canada Department of Mathematical and Computational Sciences University of Toronto Mississauga Canada Institute of Biomedical Engineering University of Toronto Canada

A technical challenge in robotic soft material cutting is to avoid large local deformations that result in inaccuracies or failure of the task. Additionally, reducing procedure time and human errors that occur due to fatigue and monotony are two of the most anticipated advantages of using robots in execution of repetitive subtasks for minimally invasive surgery. In our paper, we evaluate pattern cutting tasks of 2D elastic materials with shared control using the da Vinci Research Kit (dVRK). For this purpose, we developed a shared autonomy motion generator framework for pattern cutting. The framework registers user-defined Cartesian positions, creates smooth splines, interpolates the Cartesian positions, and generates a trajectory with Cartesian and joint constraints. While a pre-planned trajectory is being executed, the user may provide Cartesian offsets to modify the trajectory. We repeatedly cut shapes on 3 materials with different elasticity. Our shared control method achieved 100% success rate while performing a circular cutting task in a sheet of gauze. The user input compensated for deformations due to tearing. Task completion time of those experiments was 86 seconds (median) / 88 seconds (mean). Median and mean errors were 3.1 mm and 3 mm, respectively. Our work improves the success rate and time of completion of published pattern cutting tasks.

关键词： Minimally invasive surgery Medical robotics Deformation Shape Fatigue Generators Trajectory

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Game-theoretic Utility Tree for Multi-Robot Cooperative Pursuit Strategy

arXiv

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arXiv 2022年

作者： Yang, Qin Parasuraman, Ramviyas The Heterogeneous Robotics Lab Department of Computer Science University of Georgia AthensGA30602 United States

Underlying relationships among multiagent systems (MAS) in hazardous scenarios can be represented as game-theoretic models. In adversarial environments, the adversaries can be intentional or unintentional based on their needs and motivations. Agents will adopt suitable decision-making strategies to maximize their current needs and minimize their expected costs. This paper proposes and extends the new hierarchical network-based model, termed Game-theoretic Utility Tree (GUT), to arrive at a cooperative pursuit strategy to catch an evader in the Pursuit-Evasion game domain. We verify and demonstrate the performance of the proposed method using the Robotarium platform compared to the conventional constant bearing (CB) and pure pursuit (PP) strategies. The experiments demonstrated the effectiveness of the GUT, and the performances validated that the GUT could effectively organize cooperation strategies, helping the group with fewer advantages achieve higher performance. Copyright © 2022, The Authors. All rights reserved.

关键词： Game theory

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A Hybrid EEG-EMG Framework for Humanoid Control using Deep Learning Transformers

A Hybrid EEG-EMG Framework for Humanoid Control using Deep L...

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IEEE International Conference on robotics and Biomimetics

作者： Parthan Olikkal Branesh M. Pillai Jackrit Suthakorn Habib Ali Ramana Vinjamuri Department of Computer Science and Electrical Engineering Vinjamuri lab University of Maryland Baltimore County Baltimore MD USA Center for Biomedical and Robotics Technology Mahidol University Salaya Nakorm Pathom Thailand

ISBN: (数字)9798331509644

ISBN: (纸本)9798331509651

Brain-computer interfaces (BCIs) offer promising solutions for upper limb rehabilitation. Despite advancements in deep learning, traditional models for motor rehabilitation using electroencephalography (EEG) or electromyography (EMG) to control assistive devices require enhancement. This study aims to enhance motor control capabilities by integrating EEG and EMG signals using a Transformer-based deep learning model. Ten able-bodies subjects performed center-out tasks on a low-cost upper limb rehabilitation table, capturing 2D kinematic data, EEG, and EMG signals simultaneously. The tasks varied in complexity across four levels. Preprocessed EEG and EMG signals were fused and given as input to the proposed model, which was evaluated using three performance metrics. Results showed that the EEG-EMG combined model achieved 87.27% accuracy across all the four levels. Furthermore, the model's output successfully controlled a humanoid robot to replicate similar movements. These findings highlight the efficacy of combined EEG-EMG data in improving accuracy and performance in BCI applications, advancing assistive technologies and neurorehabilitation interventions.

关键词： Deep learning Accuracy Humanoid robots Brain modeling Transformers Motors Electromyography Electroencephalography Data models Brain-computer interfaces

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PREDICTING GENDER AND POLITICAL AFFILIATION USING MOBILE PAYMENT DATA

arXiv

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arXiv 2023年

作者： Stobaugh, Ben Murthy, Dhiraj Department of Computer Science The University of Texas Austin United States Computational Media Lab Moody College of Communication The University of Texas Austin United States

We explore the understudied area of social payments to evaluate whether or not we can predict the gender and political affiliation of Venmo users based on the content of their Venmo transactions. Latent attribute detection has been successfully applied in the domain of studying social media. However, there remains a dearth of previous work using data other than Twitter. There is also a continued need for studies which explore mobile payments spaces like Venmo, which remain understudied due to the lack of data access. We hypothesize that using methods similar to latent attribute analysis with Twitter data, machine learning algorithms will be able to predict gender and political affiliation of Venmo users with a moderate degree of accuracy. We collected crowdsourced training data that correlates participants’ political views with their public Venmo transaction history through the paid Prolific service. Additionally, we collected 21 million public Venmo transactions from recently active users to use for gender classification. We then ran the collected data through a TF-IDF vectorizer and used that to train a support vector machine (SVM). After hyperparameter training and additional feature engineering, we were able to predict user’s gender with a high level of accuracy (.91) and had modest success predicting user’s political orientation (.63). © 2023, CC BY.

关键词： Support vector machines

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QueryCAD: Grounded Question Answering for CAD Models

arXiv

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arXiv 2024年

作者： Kienle, Claudius Alt, Benjamin Katic, Darko Jäkel, Rainer Peters, Jan ArtiMinds Robotics Karlsruhe Germany IAS Lab Computer Science Department TU Darmstadt Germany AICOR Institute for Artificial Intelligence University of Bremen Germany Stuttgart University of Applied Sciences Germany

CAD models are widely used in industry and are essential for robotic automation processes. However, these models are rarely considered in novel AI-based approaches, such as the automatic synthesis of robot programs, as there are no readily available methods that would allow CAD models to be incorporated for the analysis, interpretation, or extraction of information. To address these limitations, we propose QueryCAD, the first system designed for CAD question answering, enabling the extraction of precise information from CAD models using natural language queries. QueryCAD incorporates SegCAD, an open-vocabulary instance segmentation model we developed to identify and select specific parts of the CAD model based on part descriptions. We further propose a CAD question answering benchmark to evaluate QueryCAD and establish a foundation for future research. Lastly, we integrate QueryCAD within an automatic robot program synthesis framework, validating its ability to enhance deep-learning solutions for robotics by enabling them to process CAD models. https://***/querycad © 2024, CC BY-NC-SA.

关键词： Robot programming

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Learning Inverse Kinematics Multiplicity of Concentric Tube Robots Using Invertible Neural Networks

Learning Inverse Kinematics Multiplicity of Concentric Tube ...

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International Symposium on Medical robotics (ISMR)

作者： Paul H. Kang Radian Gondokaryono Majid Roshanfar Robert H. Nguyen Thomas Looi James M. Drake Dale Podolsky Institute of Biomedical Engineering University of Toronto Canada Wilfred and Joyce Posluns Centre for Image Guided Innovation and Therapeutic Intervention Hospital for Sick Children Toronto Canada Department of Computer Science Medical Computer Vision and Robotics Lab University of Toronto Canada

ISBN: (数字)9798331599003

ISBN: (纸本)9798331599010

Concentric tube robots (CTR) are a promising technology for medical applications due to their small size, flexibility, and ability to make complex shapes. These robots are built from a series of pre-curved, super-elastic tubes that are arranged concentrically and manipulated through rotational and translational movements at their proximal ends. Achieving accurate kinematics is essential in making CTRs useful in minimally invasive surgical procedures where precision and safety is paramount. Due to the difficulty of incorporating nonlinear effects like friction and tube clearances into analytical models, previous works have investigated machine learningbased models for CTR kinematics, leading to higher kinematic accuracies. We present a kinematic model for CTRs using an invertible neural network architecture that, unlike other learningbased models, can generate multiple inverse kinematic solutions. Our model achieved a mean forward kinematic tip error of 2.86 mm ( $\mathbf{3. 4 3 \%}$ normalized to arclength), outperforming a Cosserat rod-based analytical CTR model. In three test cases (two static points and a circle trajectory), our model achieved mean inverse kinematic errors of $6.19,3.33$ , and $3.86 ~\text{mm}(5.66 \%, 4.12 \%$ , and 4.16 % normalized to arclength). We additionally present a robust data capture pipeline that is able to reconstruct the CTR's shape that uses state-of-the-art segmentation models.

关键词： Analytical models Concentric tube robots Accuracy Minimally invasive surgery Shape Neural networks Pipelines Kinematics Trajectory Safety

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Improving Formation in Swarm robotics with a Leader-Follower Approach

Improving Formation in Swarm Robotics with a Leader-Follower...

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IEEE International Conference on Mechatronics and Automation

作者： Mazen Bahaidarah Ognjen Marjanovic Fatemeh Rekabi-Bana Farshad Arvin Department of Electrical & Electronic Engineering University of Manchester Manchester UK King Abdulaziz University Jeddah Saudi Arabia Department of Computer Science Swarm & Computational Intelligence Lab (SwaCIL) Durham University Durham UK

ISBN: (数字)9798350388077

ISBN: (纸本)9798350388084

Collective Motion (CM) is a bio-inspired swarm behaviour observed in nature, such as schooling fish and flocking birds. In swarm robotics, CM is attained through the implementation of virtual viscoelastic links that generate attractive and repulsive forces among robots. This study investigates the challenges associated with maintaining formation integrity in specific geometrical arrangements, such as ring configurations. To address this, a Leader-Follower (L-F) approach is proposed to enhance the swarm shape stability. Simulation results demonstrated that the proposed method significantly optimised CM performance, achieving alignment of nearly 99% of the robots within 50 s. This shows the effectiveness of the L-F approach in successfully implementing swarm formation with a significant performance improvement.

关键词： Mechatronics Shape Navigation Robot kinematics Simulation Noise Swarm robotics

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Byzantine Robust Aggregation in Federated Distillation with Adversaries

Byzantine Robust Aggregation in Federated Distillation with ...

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International Conference on Distributed Computing Systems

作者： Wenrui Li Hanlin Gu Sheng Wan Zhirong Luan Wei Xi Lixin Fan Qiang Yang Badong Chen Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University Xi'an China WeBank AI Lab Shenzhen China Department of Computer Science Hong Kong University of Science and Technology Hong Kong China School of Electrical Engineering Xi'an University of Technology Xi'an China Department of Computer Science Xi'an Jiaotong University Xi'an China

ISBN: (数字)9798350386059

ISBN: (纸本)9798350386066

Federated learning empowers privacy-preserving, multi-party secure model training without the necessity of sharing raw data. In recent years, knowledge distillation has emerged as a promising solution to address the significant challenge of model heterogeneity within federated learning. However, current research often overlooks the potential threats posed by Byzantine attacks, which can significantly compromise the security of federated distillation. Previous work on Byzantine attacks has been primarily focused on manipulating local gradients to compromise global model, lacking attacks on logits in knowledge distillation scenarios. In this paper, we introduce two innovative attacks, shedding light on the inherent risks in federated distillation. The proposed attacks include a top-k attack, which perturbs the top k values of logits in each column, and an impersonation attack, which emulates knowledge significantly deviating from the norm. To counter such attacks, we propose a robust aggregation strategy-FedTGD (Federated Top Guard Distillation), designed to ensure robust distillation with heterogeneous models. Specifically, FedTGD incorporates Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and maximum cosine similarity on top-k values of logits to select benign knowledge. Experimental evaluations conducted on FEMNIST and CIFAR100 datasets, considering scenarios for both IID and Non-IID, reveal that top-k attack results in a substantial 27.16% accuracy reduction for FedMD. In contrast, our aggregation method shows a marginal 0.7% accuracy decrease under top-k attacks, outperforming state-of-the-art baselines.

关键词： Training Accuracy Federated learning computational modeling Noise Impersonation attacks Data models

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Multi-Robot Synergistic Localization in Dynamic Environments

arXiv

引用

arXiv 2022年

作者： Latif, Ehsan Parasuraman, Ramviyas The Heterogeneous Robotics Lab Department of Computer Science University of Georgia AthensGA30602 United States

A mobile robot’s precise location information is critical for navigation and task processing, especially for a multirobot system (MRS) to collaborate and collect valuable data from the field. However, a robot in situations where it does not have access to GPS signals, such as in an environmentally controlled, indoor, or underground environment, finds it difficult to locate using its sensor alone. As a result, robots sharing their local information to improve their localization estimates benefit the entire MRS team. There have been several attempts to model-based multi-robot localization using Radio Signal Strength Indicator (RSSI) as a source to calculate bearing information. We also utilize the RSSI for wireless networks generated through the communication of multiple robots in a system and aim to localize agents with high accuracy and efficiency in a dynamic environment for shared information fusion to refine the localization estimation. This estimator structure reduces one source of measurement correlation while appropriately incorporating others. This paper proposes a decentralized Multi-robot Synergistic Localization System (MRSL) for a dense and dynamic environment. Robots update their position estimation whenever new information receives from their neighbors. When the system senses the presence of other robots in the region, it exchanges position estimates and merges the received data to improve its localization accuracy. Our approach uses Bayesian rule-based integration, which has shown to be computationally efficient and applicable to asynchronous robotics communication. We have performed extensive simulation experiments with a varying number of robots to analyze the algorithm. MRSL’s localization accuracy with RSSI outperformed others on any number of robots, 66% higher than autonomous robot localization (ARL) (which works without collaboration between robots) and 32% higher than the collaborative multi-robot algorithm from the literature. The simulation r

关键词： Wireless sensor networks

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DACOOP-A: Decentralized Adaptive Cooperative Pursuit via Attention

arXiv

引用

arXiv 2023年

作者： Zhang, Zheng Zhang, Dengyu Zhang, Qingrui Pan, Wei Hu, Tianjiang Lab. Sun Yat-Sen University Shenzhen China Department of Computer Science The University of Manchester United Kingdom Department of Cognitive Robotics Delft University of Technology Netherlands

Integrating rule-based policies into reinforcement learning promises to improve data efficiency and generalization in cooperative pursuit problems. However, most implementations do not properly distinguish the influence of neighboring robots in observation embedding or inter-robot interaction rules, leading to information loss and inefficient cooperation. This paper proposes a cooperative pursuit algorithm named Decentralized Adaptive COOperative Pursuit via Attention (DACOOP-A) by empowering reinforcement learning with artificial potential field and attention mechanisms. An attention-based framework is developed to emphasize important neighbors by concurrently integrating the learned attention scores into observation embedding and inter-robot interaction rules. A KL divergence regularization is introduced to alleviate the resultant learning stability issue. Improvements in data efficiency and generalization are demonstrated through numerical simulations. Extensive quantitative analysis and ablation studies are performed to illustrate the advantages of the proposed modules. Real-world experiments are performed to justify the feasibility of deploying DACOOP-A in physical systems. Copyright © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

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