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11th RSI International Conference on robotics and Mechatronics, ICRoM 2023

作者： Fateh, Alireza Momeni, Hamidreza Masouleh, Mehdi Tale Tarbiat Modares University Dept. of Electrical and Computer Eng Tehran Iran University of Tehran Human and Robot Interaction Lab School of Electrical and Computer Eng Tehran Iran

ISBN: (纸本)9798350308105

Time Delay Control (TDC) is recognized as a simple yet effective alternative to model-based or intelligent-based control due to its simplicity and robustness. Proportional-Integral-Derivative (PID) control is one of the industry's most used and straightforward control schemes. While this approach has proven to be effective, the tuning of its coefficients has remained a difficult task. Though showing similar performance, a definite connection between the two approaches has yet to be defined. In this paper, by taking advantage of padé approximation, a direct connection between the two control methods coefficients is obtained. As a result, a new approach for choosing the classical PID controller coefficients is introduced using TDC as the primary control scheme design framework, which has led to a decrease in the number of required control design coefficients of the PID control system. Further on, the uniformly ultimately bounded stability of the control system under bounded external disturbances is proven. Finally, via simulations on the SCARA robot, the obtained results and the validity of the proposed connection are explored. © 2023 IEEE.

关键词： Time delay

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robots as People Theoretical Approaches, Ethics, and Design 6

Robots as People Theoretical Approaches, Ethics, and Design

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6th Social robots with AI: Prospects, Risks, and Responsible Methods Robophilosophy, RP 2024

作者： Berio, Leda Blatter, Jonas Winkle, Katie Förster, Frank Fischer, Kerstin Dept. for Philosophy Ruhr University Bochum Germany Department for Information Technology Uppsala University Sweden Adaptive Systems Research Group University of Hertfordshire United Kingdom Human-Robot Interaction Lab University of Southern Denmark Denmark

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Scene Understanding in Pick-and-Place Tasks: Analyzing Transformations Between Initial and Final Scenes

Scene Understanding in Pick-and-Place Tasks: Analyzing Trans...

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Iranian Conference on Electrical Engineering (ICEE)

作者： Seraj Ghasemi Hamed Hosseini MohammadHossein Koosheshi Mehdi Tale Masouleh Ahmad Kalhor Human and Robot Interaction Lab School of Electrical and Computer Engineering University of Tehran Tehran Iran

ISBN: (数字)9798350376340

ISBN: (纸本)9798350376357

With robots increasingly collaborating with humans in everyday tasks, it is important to take steps toward robotic systems capable of understanding the environment. This work focuses on scene understanding to detect pick-and-place tasks given initial and final images from the scene. To this end, a dataset is collected for object detection and pick-and-place task detection. A YOLOv5 network is subsequently trained to detect the objects in the initial and final scenes. Given the detected objects and their bounding boxes, two methods are proposed to detect the pick-and-place tasks which transform the initial scene into the final scene. A geometric method is proposed which tracks objects' movements in the two scenes and works based on the intersection of the bounding boxes which moved within scenes. Contrarily, the CNN-based method utilizes a Convolutional Neural Network to classify objects with intersected bounding boxes into 5 classes, showing the spatial relationship between the involved objects. The performed pick-and-place tasks are then derived from analyzing the experiments with both scenes. Results show that CNN-based method, by VGG-16 backbone, outscores the geometric method by roughly 12% in certain scenarios, with an overall success rate of 84.3%.

关键词： YOLO Electrical engineering Accuracy Tracking Transforms Feature extraction Manipulators

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Scene Understanding in Pick-and-Place Tasks: Analyzing Transformations Between Initial and Final Scenes

arXiv

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arXiv 2024年

作者： Ghasemi, Seraj Hosseini, Hamed Koosheshi, MohammadHossein Masouleh, Mehdi Tale Kalhor, Ahmad Human and Robot Interaction Lab School of Electrical and Computer Engineering University of Tehran Tehran Iran

With robots increasingly collaborating with humans in everyday tasks, it is important to take steps toward robotic systems capable of understanding the environment. This work focuses on scene understanding to detect pick-and-place tasks given initial and final images from the scene. To this end, a dataset is collected for object detection and pick-and-place task detection. A YOLOv5 network is subsequently trained to detect the objects in the initial and final scenes. Given the detected objects and their bounding boxes, two methods are proposed to detect the pick-and-place tasks which transform the initial scene into the final scene. A geometric method is proposed which tracks objects’ movements in the two scenes and works based on the intersection of the bounding boxes which moved within scenes. Contrarily, the CNN-based method utilizes a Convolutional Neural Network to classify objects with intersected bounding boxes into 5 classes, showing the spatial relationship between the involved objects. The performed pick-and-place tasks are then derived from analyzing the experiments with both scenes. Results show that CNN-based method, by VGG-16 backbone, outscores the geometric method by roughly 12% in certain scenarios, with an overall success rate of 84.3%.MSC Codes Computer Vision and Pattern Recognition (***), Systems and Control (***) © 2024, CC BY.

关键词： Convolutional neural networks

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Generating a General Culturing Microorganism Pattern Using a Delta Parallel robot and Cam-in-Hand Calibration Method

Generating a General Culturing Microorganism Pattern Using a...

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RSI/ISM International Conference on robotics and Mechatronics (ICROM)

作者： Navid Pasiar Arvin Mohammadi Sajjad Ahangar Navid Asadi Khomami Mehdi Tale Masouleh Department of Mechanical Eng. Human and Robot Interaction Lab. University of Tehran Tehran Iran Human and Robot Interaction Lab. University of Tehran Tehran Iran Human and Robot Interaction Lab. School of Electrical and Computer Eng. University of Tehran Tehran Iran

robots can play a vital role in laboratory tasks, especially in culturing microorganisms. Currently, many of these operations are performed manually, which leads to biased and irreproducible results. This paper explores a calibration method for minimizing errors of a performing two certain laboratory tasks of swabbing and pipetting. This leads to enhanced accuracy and productivity. This research intends to culture microorganisms in a petri-dish in a specific pattern, measuring the coordinates of each object in the output image so the object location in the real world can be calculated. This research investigates the Cam-in-Hand method for a Delta Parallel robot, meaning that the camera is mounted on the End-Effector. In this approach, a method of translating image coordinates into real-world coordinates is introduced. It is essential to provide an additional coordinate (in this case $z -$value) before the conversion, given that the camera outputs a 2D image. A vector-based image, in SVG format, as input to the algorithm generates a set of coordinates which determines the main points for swabbing or pipetting operation. Using this data, an array of coordinates is linearly interpolated in 3D space for the swabbing operation. Conversely, the trajectory generated for pipetting uses the 4-5-6-7 interpolating polynomial. The robot then adheres to following the interpolated array of coordinates as a function of time, using a PID controller. The pipetting device is built with a 3D printer using PLA materials. The calibration is done in different heights. The Camin-Hand method leads to ±1 cm precision as a result. Minimum, maximum, and mean errors are 0.092,1.61 and 0.76 respectively for the central point.

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robotic Sorting of Mechanical and Electrical Parts: An Autonomous Vision-Based Approach in a Practical Case Study

Robotic Sorting of Mechanical and Electrical Parts: An Auton...

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International Conference on Control, Instrumentation, and Automation

作者： Mohammad Yousefi Mahdi Dalvand Navid Asadi Khomami Mehdi Tale Masouleh Mohammad Reza Haeri Yazdi Human and Robot Interaction Lab. School of Mechanical Engineering University of Tehran Tehran Iran Human and Robot Interaction Lab. School of Electrical and Computer Engineering University of Tehran Tehran Iran School of Mechanical Engineering University of Tehran Tehran Iran

ISBN: (数字)9798331509972

ISBN: (纸本)9798331509989

In today’s industrial landscape, automation has become increasingly vital, particularly in the deployment of robots for tasks such as sorting machine components. The use of robotic systems enhances process accuracy and speed, resulting in significant cost reductions and improved productivity compared to manual labor. This paper aims to design and develop an automated sorting system for various types of mechanical and electrical parts, utilizing image processing and machine vision algorithms with a Delta parallel robot equipped with a two-finger gripper. The target mechanical and electrical parts in this study are screws, nuts, metal washers, rubber washers, retaining rings, rectangular keys, wall plugs, resistors, potentiometers, capacitors, batteries, ICs, and LEDs. The YOLOv3 algorithm and Adaptive Thresholding method are employed to detect and distinguish objects from the background, and size measurement is achieved with the help of a custom marker with known dimensions. In this study, transfer learning based on pre-trained weights of YOLOv3 for the COCO dataset is applied. The proposed system attains a final mean Average Precision (mAP@0.5) value exceeding 0.95 for part detection using YOLOv3. Additionally, it demonstrates an overall pick-and-place success rate exceeding $\mathbf{9 0 \%}$.

关键词： YOLO Parallel robots Automation Thresholding (Imaging) Service robots robot kinematics Transfer learning Size measurement Grippers Sorting

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Towards Passive Safe Reinforcement Learning: A Comparative Study on Contact-rich robotic Manipulation

arXiv

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arXiv 2025年

作者： Zhang, Heng Solak, Gokhan Hjorth, Sebastian Ajoudani, Arash Human-Robot Interfaces and Interaction Lab Istituto Italiano di Tecnologia Genoa Italy Università di Genova Genoa Italy

Reinforcement learning (RL) has achieved remarkable success in various robotic tasks;however, its deployment in real-world scenarios, particularly in contact-rich environments, often overlooks critical safety and stability aspects. Policies without passivity guarantees can result in system instability, posing risks to robots, their environments, and human operators. In this work, we investigate the limitations of traditional RL policies when deployed in contact-rich tasks and explore the combination of energy-based passive control with safe RL in both training and deployment to answer these challenges. Firstly, we introduce energy-based constraints in our safe RL formulation to train passivity-aware RL agents. Secondly, we add a passivity filter on the agent output for passivity-ensured control during deployment. We conduct comparative studies on a contact-rich robotic maze exploration task, evaluating the effects of learning passivity-aware policies and the importance of passivity-ensured control. The experiments demonstrate that a passivity-agnostic RL policy easily violates energy constraints in deployment, even though it achieves high task completion in training. The results show that our proposed approach guarantees control stability through passivity filtering and improves the energy efficiency through passivity-aware training. A video of real-world experiments is available as supplementary material. We also release the checkpoint model and offline data for pre-training at Hugging Face © 2025, CC BY.

关键词： Reinforcement learning

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Real-Time Imitation of human Head Motions, Blinks and Emotions by Nao robot: A Closed-Loop Approach 11

Real-Time Imitation of Human Head Motions, Blinks and Emotio...

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11th RSI International Conference on robotics and Mechatronics, ICRoM 2023

作者： Rayati, Keyhan Feizi, Amirhossein Beigy, Alireza Shahverdi, Pourya Masouleh, Mehdi Tale Kalhor, Ahmad Louie, Wing-Yue Geoffrey University of Tehran Human and Robot Interaction Lab Electrical and Computer Engineering Tehran Iran Shahrood University of Technology Department of Electrical Engineering Shahrood Iran Oakland University Intelligent Robotics Laboratory Michigan United States

ISBN: (纸本)9798350308105

This paper introduces a novel approach for enabling real-time imitation of human head motion by a Nao robot, with a primary focus on elevating human-robot interactions. By using the robust capabilities of the MediaPipe as a computer vision library and the DeepFace as an emotion recognition library, this research endeavors to capture the subtleties of human head motion, including blink actions and emotional expressions, and seamlessly incorporate these indicators into the robot's responses. The result is a comprehensive framework which facilitates precise head imitation within human-robot interactions, utilizing a closed-loop approach that involves gathering real-time feedback from the robot's imitation performance. This feedback loop ensures a high degree of accuracy in modeling head motion, as evidenced by an impressive R2 score of 96.3 for pitch and 98.9 for yaw. Notably, the proposed approach holds promise in improving communication for children with autism, offering them a valuable tool for more effective interaction. In essence, proposed work explores the integration of real-time head imitation and real-time emotion recognition to enhance human-robot interactions, with potential benefits for individuals with unique communication needs. © 2023 IEEE.

关键词： Emotion Recognition

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Flying SD Cards, Aerial Repeaters, & Homebrew Apps: Emergent Use of Technologies for Collaboration in Search and Rescue 20

Flying SD Cards, Aerial Repeaters, & Homebrew Apps: Emergent...

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20th Global Information Systems for Crisis Response and Management Conference, ISCRAM 2023

作者： LaLone, Nicolas Van Natta, Jared Cormier, Michelle V. Fraune, Marlena R. Hamilton, Bill Toups Dugas, Phoebe O. Alharthi, Sultan A. Zemedie, Mahlet University of Nebraska Omaha United States New Mexico State University United States Intergroup Human-Robot Interaction Lab Department of Psychology New Mexico State University United States University of Jeddah Saudi Arabia

ISBN: (纸本)9798218217495

Search and rescue (SAR) teams are the first to respond to emergencies. This could include finding lost hikers, shoring buildings, or aiding people post-disaster. SAR combines orienteering, engineering, field medicine, and communication. Technology use in SAR has been changing with the proliferation of information communication technologies;so, we ask, how are established and emerging technologies used in SAR? Understanding how responders are adopting and adapting these technologies during SAR missions can inform future design and improve outcomes for SAR teams. We interviewed SAR volunteers to contextualize their experiences with technology and triangulated with additional questionnaire data. We discuss how technology use in SAR requires an intersection of expert knowledge and creative problem solving to overcome challenges in the field. This research contributes an understanding of the constraints on and implications for future SAR technologies and SAR operators’ creativity in emergent situations. © 2023 Information Systems for Crisis Response and Management, ISCRAM. All rights reserved.

关键词： Antennas

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SRL-VIC: A Variable Stiffness-Based Safe Reinforcement Learning for Contact-Rich robotic Tasks

arXiv

引用

arXiv 2024年

作者： Zhang, Heng Solak, Gokhan Lahr, Gustavo J.G. Ajoudani, Arash Human-Robot Interfaces and Interaction Lab Istituto Italiano di Tecnologia Genoa Italy Università di Genova Genoa Italy

Reinforcement learning (RL) has emerged as a promising paradigm in complex and continuous robotic tasks, however, safe exploration has been one of the main challenges, especially in contact-rich manipulation tasks in unstructured environments. Focusing on this issue, we propose SRL-VIC: a model-free safe RL framework combined with a variable impedance controller (VIC). Specifically, safety critic and recovery policy networks are pre-trained where safety critic evaluates the safety of the next action using a risk value before it is executed and the recovery policy suggests a corrective action if the risk value is high. Furthermore, the policies are updated online where the task policy not only achieves the task but also modulates the stiffness parameters to keep a safe and compliant profile. A set of experiments in contact-rich maze tasks demonstrate that our framework outperforms the baselines (without the recovery mechanism and without the VIC), yielding a good trade-off between efficient task accomplishment and safety guarantee. We show our policy trained on simulation can be deployed on a physical robot without fine-tuning, achieving successful task completion with robustness and generalization. The video is available at https://***/ksWXR3vByoQ. © 2024, CC BY.

关键词： robotics

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