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2023 IEEE International Conference on Big Data, BigData 2023

作者： Akter, Mst Shapna Shahriar, Hossain Cuzzocrea, Alfredo Wu, Fan Rodriguez-Cardenas, Juanjose University of West Florida Department of Intelligent Systems and Robotics United States University of West Florida Center of Cybersecurity United States University of Calabria IDEA Lab Rende Italy Tuskegee University Dept. of Computer Science Tuskegee United States Kennesaw State University Dept. of Information Technology Georgia United States

ISBN: (纸本)9798350324457

Social media cyberbullying has a detrimental effect on human life. As online social networking grows daily, the amount of hate speech also increases. Such terrible content can cause depression and actions related to suicide. This paper proposes a trustable LSTM-Autoencoder Network for cyberbullying detection on social media using synthetic data. We have demonstrated a cutting-edge method to address data availability difficulties by producing machine-translated data. However, several languages such as Hindi and Bangla still lack adequate investigations due to a lack of datasets. We carried out experimental identification of aggressive comments on Hindi, Bangla, and English datasets using the proposed model and traditional models, including Long Short-Term Memory (LSTM), Bidirectional Long ShortTerm Memory (BiLSTM), LSTM-Autoencoder, Word2vec, Bidirectional Encoder Representations from Transformers (BERT), and Generative Pre-trained Transformer 2 (GPT-2) models. We employed evaluation metrics such as f1-score, accuracy, precision, and recall to assess the models' performance. Our proposed model outperformed all the models on all datasets, achieving the highest accuracy of 95%. Our model achieves state-of-the-art results among all the previous works on the dataset we used in this paper. © 2023 IEEE.

关键词： Cyber-bullying Deep Learning Natural Language Processing Neural Networks

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A Compliant Traction Method of Manipulator Based on Online Calibration of Force Sensor Zero-Point 6

A Compliant Traction Method of Manipulator Based on Online C...

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6th International Conference on Automation, Control and Robots, ICACR 2022

作者： Xu, Zhilu Wang, Weijun Feng, Wei Chinese Academy of Sciences Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Shenzhen China Shenzhen Institute of Advanced Technology Cas Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen China

ISBN: (数字)9781665498739

ISBN: (纸本)9781665498739

With the increasing number of cooperative robots, it is becoming increasingly important to improve the safety and operability of human-robot interaction. Installing a six-dimensional force sensor at the end of the manipulator can realize the drag of the manipulator in Cartesian space. A compliant traction method of manipulator based on online calibration of force sensor zero-point is proposed in this paper. Aiming at the problem of "zero drift"of force sensor, after calculating the gravity of the end tool, the zero-point of the force sensor is calibrated online by means of mean processing, so as to obtain more accurate external force. Then, the relationship between the end position of the manipulator and the external force, the end moving speed of the manipulator and the external force is established, and the change of the position and speed of the manipulator is controlled by the external force at the same time. Finally, experiments show the effectiveness of this method and ensure the safety and reliability in the traction process. © 2022 IEEE.

关键词： Manipulators

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TelePulse: Enhancing the Teleoperation Experience through Biomechanical Simulation-Based Electrical Muscle Stimulation in Virtual Reality 25

TelePulse: Enhancing the Teleoperation Experience through Bi...

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Proceedings of the 2025 CHI Conference on Human Factors in Computing systems

作者： Seokhyun Hwang Seongjun Kang Jeongseok Oh Jeongju Park Semoo Shin Yiyue Luo Joseph DelPreto Sangbeom Lee Kyoobin Lee Wojciech Matusik Daniela Rus SeungJun Kim Information School University of Washington Seattle Washington USA and Human-Centered Intelligent Systems Lab Gwangju Institute of Science and Technology Gwangju Republic of Korea Human-Centered Intelligent Systems Lab Gwangju Institute of Science and Technology Gwangju Republic of Korea ECE University of Washington Seattle Washington USA Distributed Robotics Lab Massachusetts Institute of Technology (MIT) Cambridge Massachusetts USA Artificial Intelligence Lab Gwangju Institute of Science and Technology Gwangju Republic of Korea CSAIL MIT Cambridge Massachusetts USA Distributed Robotics Lab Massachusetts Institute of Technology Cambridge Massachusetts USA

ISBN: (纸本)9798400713941

This paper introduces TelePulse, a system integrating biomechanical simulation with electrical muscle stimulation (EMS) to provide precise haptic feedback for robot teleoperation tasks in virtual reality (VR). TelePulse has two components: a physical simulation part that calculates joint torques based on real-time force data from remote manipulators, and an electrical stimulation part that converts these torques into muscle stimulation. Two experiments were conducted to evaluate the system. The first experiment assessed the accuracy of EMS generated through biomechanical simulations by comparing it with electromyography (EMG) data during force-directed tasks, while the second experiment evaluated the impact of TelePulse on teleoperation performance during sanding and drilling tasks. The results suggest that TelePulse provided more accurate stimulation across all arm muscles, thereby enhancing task performance and user experience in the teleoperation environment. In this paper, we discuss the effect of TelePulse on teleoperation, its limitations, and areas for future improvement. © 2025 Copyright held by the owner/author(s).

关键词： Teleoperation Virtual Reality Haptic Electrical Muscle Stimulation Biomechanics Simulation Wearable Device

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A Comparison of Imitation Learning Pipelines for Autonomous Driving on the Effect of Change in Ego-vehicle

A Comparison of Imitation Learning Pipelines for Autonomous ...

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IEEE Symposium on intelligent Vehicle

作者： Noorsyamimi Abdur Ajak Wee Hong Ong Owais Ahmed Malik Robotics and Intelligent Systems Lab (Robolab) School of Digital Science Universiti Brunei Darussalam Jalan Tungku Link Brunei School of Digital Science Universiti Brunei Darussalam Jalan Tungku Link Brunei

ISBN: (数字)9798350348811

ISBN: (纸本)9798350348828

This paper presents a comparison of the effect of change in ego-vehicle in two different pipelines of imitation learning for autonomous driving (AD) between direct control-based and waypoint-based pipelines. Control-based pipeline involves predicting control signals directly to control the car, whereas a waypoint-based pipeline predicts the future trajectory of the car and uses a controller module to generate the control signals from the predicted waypoints. In this study, CIL++ was used for the control-based method whereas TransFuser was used for the waypoint-based method. In our experiments, we used CARLA simulator and deployed both imitation learning models, without retraining or re-tuning the controller parameters, on various cars different from the car used during training. We used Town05 from CARLA’s Leaderboard benchmark to evaluate the performance based on driving score, the main metric used in the benchmark. Based on the experiment results, TransFuser is more robust in adapting to different ego-vehicles than CIL++. TransFuser performed better when deployed to different vehicles. However, the performance still suffered when there was a significant change in the car classes. The source code of this work is made publicly available at https://***/ailabspace/Comparisonof-Autonomous-Driving-IL-Pipeline-for-Ego-Vehicle-Changes.

关键词： Training Measurement Imitation learning Source coding Pipelines Benchmark testing Trajectory

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Probabilistic Spiking Neural Network for Robotic Tactile Continual Learning

Probabilistic Spiking Neural Network for Robotic Tactile Con...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Senlin Fang Yiwen Liu Chengliang Liu Jingnan Wang Yuanzhe Su Yupo Zhang Hoiio Kong Zhengkun Yi Xinyu Wu City University of Macau Macau China The Department of Intelligent Systems and Robot Learning Lab ISRL Group SIAT Branch Institute of Artificial Intelligence and Robotics for Society Shenzhen Institute of Advanced Technology Shenzhen

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

The sense of touch is essential for robots to perform various daily tasks. Artificial Neural Networks have shown significant promise in advancing robotic tactile learning. However, due to the changing of tactile data distribution as robots encounter new tasks, ANN-based robotic tactile learning suffers from catastrophic forgetting. To solve this problem, we introduce a novel continual learning (CL) framework called the Probabilistic Spiking Neural Network with Variational Continual Learning (PSNN-VCL). In this framework, PSNN introduces uncertainty during spike emission and can apply fast Variational Inference by optimizing the uncertainty through backpropagation, which significantly reduces the required model parameters for VCL. We establish a robotic tactile CL benchmark using publicly available datasets to evaluate our method. Experimental results demonstrated that, compared to other CL methods, PSNN-VCL not only achieves superior performance in terms of widely used CL metrics but also achieves at least a 50% reduction in model parameters on the robotic tactile CL benchmark.

关键词： Continuing education Measurement Uncertainty Spiking neural networks Learning (artificial intelligence) Benchmark testing Robot sensing systems

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ST-RRT*: Asymptotically-Optimal Bidirectional Motion Planning through Space-Time

arXiv

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arXiv 2022年

作者： Grothe, Francesco Hartmann, Valentin N. Orthey, Andreas Toussaint, Marc Learning and Intelligent Systems Group TU Berlin Germany Machine Learning & Robotics Lab University of Stuttgart Germany

We present a motion planner for planning through space-time with dynamic obstacles, velocity constraints, and unknown arrival time. Our algorithm, Space-Time RRT* (ST-RRT*), is a probabilistically complete, bidirectional motion planning algorithm, which is asymptotically optimal with respect to the shortest arrival time. We experimentally evaluate ST-RRT* in both abstract (2D disk, 8D disk in cluttered spaces, and on a narrow passage problem), and simulated robotic path planning problems (sequential planning of 8DoF mobile robots, and 7DoF robotic arms). The proposed planner outperforms RRT-Connect and RRT* on both initial solution time, and attained final solution cost. The code for ST-RRT* is available in the Open Motion Planning Library (OMPL). Copyright © 2022, The Authors. All rights reserved.

关键词： Motion planning

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Robustness of Log-Linear Learning in Network Coordination Games with Stubborn Players

Robustness of Log-Linear Learning in Network Coordination Ga...

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IEEE Conference on Decision and Control

作者： Aqsa Shahzadi Akber Hassan Jaleel Department of Electrical Engineering Intelligent Machines & Sociotechnical Systems (iMaSS) Lab Syed Babar Ali School of Science & Engineering at LUMS Lahore Pakistan NCRA National Agricultural Robotics Lab at LUMS

We analyze Log-Linear Learning (LLL) in a networked multi-agent system with stubborn players that can influence other players but do not update their actions. We are interested in the robustness of LLL against stubborn players in a coordination game setup in which the players have to decide between a status quo and an innovative practice that is inherently superior to the status quo. We investigate the impact of interaction network topology and the payoff gain offered by the innovation on the steady state behavior of the population in the presence of stubborn agent/s. We present conditions for the robustness of various networks, namely a class of 3-regular networks, $n$ × $n$ grid networks, and Erdős-Rényi (ER) random networks. For these networks, we derive the threshold values of the payoff gain for which the system behavior is robust to the presence of stubborn players under LLL.

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Training Efficient Controllers via Analytic Policy Gradient

Training Efficient Controllers via Analytic Policy Gradient

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IEEE International Conference on robotics and Automation (ICRA)

作者： Nina Wiedemann Valentin Wüest Antonio Loquercio Matthias Müller Dario Floreano Davide Scaramuzza Robotics and Perception Group University of Zurich Switzerland Laboratory of Intelligent Systems Ecole Polytechnique Fédérale de Lausanne (EPFL) Embodied AI Lab Intel

Control design for robotic systems is complex and often requires solving an optimization to follow a trajectory accurately. Online optimization approaches like Model Predictive Control (MPC) have been shown to achieve great tracking performance, but require high computing power. Conversely, learning-based offline optimization approaches, such as Reinforcement Learning (RL), allow fast and efficient execution on the robot but hardly match the accuracy of MPC in trajectory tracking tasks. In systems with limited compute, such as aerial vehicles, an accurate controller that is efficient at execution time is imperative. We propose an Analytic Policy Gradient (APG) method to tackle this problem. APG exploits the availability of differentiable simulators by training a controller offline with gradient descent on the tracking error. We address training instabilities that frequently occur with APG through curriculum learning and experiment on a widely used controls benchmark, the CartPole, and two common aerial robots, a quadrotor and a fixed-wing drone. Our proposed method outperforms both model-based and model-free RL methods in terms of tracking error. Concurrently, it achieves similar performance to MPC while requiring more than an order of magnitude less computation time. Our work provides insights into the potential of APG as a promising control method for robotics. To facilitate the exploration of APG, we open-source our code and make it available ***/lis-epfl/apg_trajectory_tracking.

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Curriculum-based Sensing Reduction in Simulation to Real-World Transfer for In-hand Manipulation

Curriculum-based Sensing Reduction in Simulation to Real-Wor...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Lingfeng Tao Jiucai Zhang Qiaojie Zheng Xiaoli Zhang Oklahoma State University Stillwater OK USA GAC R&D Center Silicon Valley Sunnyvale CA USA Colorado School of Mines Intelligent Robotics and Systems Lab Golden CO USA

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Simulation to Real-World Transfer allows affordable and fast training of learning-based robots for manipulation tasks using Deep Reinforcement Learning methods. Currently, Asymmetric Actor-Critic approaches are used for Sim2Real to reduce the rich idealized features in simulation to the accessible ones in the real world. However, the feature reduction from the simulation to the real world is conducted through an empirically defined one-step curtail. Small feature reduction does not sufficiently remove the actor’s features, which may still cause difficulty setting up the physical system, while large feature reduction may cause difficulty and inefficiency in training. To address this issue, we proposed Curriculum-based Sensing Reduction to enable the actor to start with the same rich feature space as the critic and then get rid of the hard-to-extract features step-by-step for higher training performance and better adaptation for real-world feature space. The reduced features are replaced with random signals from a Deep Random Generator to remove the dependency between the output and the removed features and avoid creating new dependencies. The methods are evaluated on the Allegro robot hand in a real-world in-hand manipulation task. The results show that our methods have faster training and higher task performance than baselines and can solve real-world tasks when selected tactile features are reduced.

关键词： Training Robot sensing systems Deep reinforcement learning Generators Sensors Task analysis

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Sensory-Motor Neurostimulation to Enhance Exosuit Performance

Sensory-Motor Neurostimulation to Enhance Exosuit Performanc...

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IEEE International Conference on Rehabilitation robotics (ICORR)

作者： Chiara Basla Lauren Chee Giacomo Valle Andrea Crema Silvestro Micera Robert Riener Stanisa Raspopovic Sensory-Motor Systems Lab Institute of Robotics and Intelligent Systems (IRIS) ETH Zurich Switzerland NeuroEngineering Lab Institute of Robotics and Intelligent Systems (IRIS) ETH Zurich Switzerland Bertarelli Foundation Chair in Translational Neuroengineering EPFL Neuro-X Institute Switzerland The BioRobotics Institute SantAnna School of Advanced Studies Pisa Italy Spinal Cord Injury Center Medical Faculty Balgrist University Hospital University of Zürich Switzerland

Exosuits typically provide limited mechanical support and rely on a user's residual functional ability. However, people with neurological impairments often suffer from both motor and sensory deficits that limit the assistance an exosuit can provide. To overcome these limitations, we developed the REINFORCE system, that complements the mechanical assistance provided by an exosuit, the Myosuit, with (1) functional electrical stimulation to enhance the activities of leg muscles, and (2) transcutaneous electrical nerve stimulation to restore somatosensory information. It consists of a fully portable and highly modular system that can be easily adapted to the level of impairment and specific need of each participant. Technical verification with three healthy participants showed reliable synchronization between all modules of the systems in all phases of walking. Additionally, we tested the system's effectiveness in one participant with multiple sclerosis who walked overground with and without functional electrical stimulation. Results showed a slight increase in self-selected walking speed (approx. 18%) and in the peak hip flexion at late swing (approx. 12%) as well as reduced step-to-step variability of step length and step time when electrical stimulation was provided. Our findings push towards a clinical trial involving more patients to validate the effectiveness of the REINFORCE system on participants' mobility.

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