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检索条件"机构=Human and Robot Interaction Lab"

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The Content of Transparency Matters: Evaluating Verbal Transparency About robot Capabilities 16th

The Content of Transparency Matters: Evaluating Verbal Trans...

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16th International Conference on Social robotics, ICSR + AI 2024

作者： Jelínek, Matouš Fischer, Kerstin Human-Robot Interaction Lab University of Southern Denmark Sønderborg Denmark

ISBN: (纸本)9789819635245

Trust is an essential aspect of human-robot interaction. For smooth and safe interactions, a calibrated level of trust – a state in which a human’s level of trust matches their robotic counterpart's capabilities – is needed. While an obvious strategy to correct possible miscalibration is transparency about the system's capabilities, previous studies are inconclusive: In some cases, the use of transparency led to an increased level of trust, in other situations, the level of trust dropped significantly. Our hypothesis is that the inconclusive results are due to the fact that what capabilities a robot is transparent about influences the effect of transparency on trust calibration. Our research therefore investigates whether transparency utterances that differ in content can either raise or dampen the perceived level of trust and competence of a robot. In an online experiment, we tested three different verbal strategies for trust calibration. Conditions differ regarding different aspects of capability the robot is transparent about: One condition uses transparency about a low level of capability, another condition uses transparency about a high level of capability. The third condition tested a robot with no additional transparency. In addition, we tested a baseline condition that used acoustic feedback only. Participants evaluated videos of four different robots, each in one of the four different conditions. We used the Multi-Dimensional Measure of Trust scale [1] to capture the perceived level of trust. Altogether 100 subjects, recruited via Prolific, participated in the experiment. Our results confirm our assumption that it matters what capabilities the robot is transparent about. Further, we confirmed that the perceived level of robots’ competence is affected by the transparency strategies used. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Nanorobots

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Kinematic-Sensitivity Indices for Double Gough-Stewart Platform 12

Kinematic-Sensitivity Indices for Double Gough-Stewart Platf...

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12th RSI International Conference on robotics and Mechatronics, ICRoM 2024

作者： Zand, Niusha Namazian, Parsa Masouleh, Mehdi Tale University of Theran Human and Robot Interaction Lab. Tehran Iran

ISBN: (纸本)9798331529734

Many indices have been introduced to optimize the architecture of a redundant robot, which is determinant in redundancy resolution of the robot. There are several ways to compute kinematic-sensitivity for dimensionally homogeneous and nonhomogeneous Jacobian matrices. The natural consequence is to use two indices that represent the kinematic-sensitivity for rotation and point-displacement, as they have been proposed by other researchers. In this paper, we compute maximum rotation and maximum displacement of end-effector of a double Gough-Stewart platform, which has a dimensionally non-homogenous Jacobian matrix, under a unit-magnitude array of actuated joint displacement. © 2024 IEEE.

关键词： End effectors

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Sound Source Localization in a 3D Multi-Surface Environment Using Learning-Based Methods 12

Sound Source Localization in a 3D Multi-Surface Environment ...

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12th RSI International Conference on robotics and Mechatronics, ICRoM 2024

作者： Esfahanian, Soroush Rayati, Keyhan Masouleh, Mehdi Tale University of Tehran Human and Robot Interaction Lab Electrical and Computer Engineering Tehran Iran

ISBN: (纸本)9798331529734

This study presents a three-dimensional localization of a ball drop in a multi-surface environment using cost-effective data acquisition devices, and proposes two learning-based methods to improve the baseline classical localization method based on GCC-PHAT-estimated TDOA values. The first proposed method employs a ball drop surface classification executed by Random Forest and XGBoost models to enhance localization performance, whereas the second method conducts region classification by using both basic and multi-branch neural network models, achieving improved and more efficient localization performance. A dataset of real-world sparse ball drop samples was collected in a controlled multi-surface environment for experimental execution, where results demonstrate that the two proposed methods improve the localization performance across various scenarios, with the region classification method significantly outperforming the baseline method, achieving a 17% reduction in the Mean Euclidean Distance error metric. Furthermore, the surface and region classification methods reduce the localization time compared to the baseline method by 50% and 97%, respectively. The obtained results reveal the potential of combining simple learning-based models with affordable microphone sensors for achieving an accurate localization of sparse audio samples in a multi-surface environment. © 2024 IEEE.

关键词： Deep neural networks

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Generating a General Culturing Microorganism Pattern Using a Delta Parallel robot and Cam-in-Hand Calibration Method 11

Generating a General Culturing Microorganism Pattern Using a...

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

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

ISBN: (纸本)9798350308105

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. © 2023 IEEE.

关键词： Calibration

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robotic Sorting of Mechanical and Electrical Parts: An Autonomous Vision-Based Approach in a Practical Case Study 10

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

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10th International Conference on Control, Instrumentation and Automation, ICCIA 2024

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

ISBN: (纸本)9798331509972

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 90%. © 2024 IEEE.

关键词： Grippers

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Autonomous Vision-based robotic Grasping of Household Objects: A Practical Case Study 11

Autonomous Vision-based Robotic Grasping of Household Object...

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

作者： Roudbari, Asal Dalvand, Mahdi Pasiar, Navid Asadi, Navid Yousefi, Mohammad Masouleh, Mehdi Tale Sharif University of Technology Electrical Engineering Department Tehran Iran University of Tehran Human Robot Interaction Lab Tehran Iran University of Tehran Human Robot Interaction Lab School of Electrical Computer Engineering Tehran Iran

ISBN: (纸本)9798350308105

Autonomous robotic grasping has emerged as a valuable capability for automating everyday household tasks like table setting (bossing), cleaning, and meal preparation. Still, accurately detecting and manipulating diverse everyday objects in unstructured home environments remains a significant challenge. In this study, an integrated robotic system combining YOLOv8s-seg deep learning for object detection and segmentation with computer vision techniques to determine optimal grasping points is proposed. The accuracy of this model has also been compared with other models through their mAP0.5, mAP.05-.95, precision and recall. A custom dataset of over 1000 images containing common household objects was created, with manual annotation and augmentation to train the YOLOv8s-seg segmentation model. The approach processes input images to understand desired arrangements and identify target objects and their locations for grasping. The detected objects are localized using camera-to-robot calibration and aligned with estimated orientations from computer vision techniques. Extensive tests have proven that representative household objects in cluttered arrangements have a grasping success rate of over 90% using a 3DOF Delta Parallel robot with a 2-finger gripper guided entirely by visual perception. By overcoming real-world challenges like transparent and reflective materials, the perception-guided robots provide precise and efficient robotic grasping in unstructured home environments. This integrated approach combines robust perception and localization, providing promising evidence for reliable automation of repetitive household organization tasks1.1A video of real-time grasp performance is uploaded as supplementary material. © 2023 IEEE.

关键词： Object detection

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AI-Driven Relocation Tracking in Dynamic Kitchen Environments 14

AI-Driven Relocation Tracking in Dynamic Kitchen Environment...

引用

14th International Conference on Computer and Knowledge Engineering, ICCKE 2024

作者： Esfahani, Arash Nasr Hosseini, Hamed Masouleh, Mehdi Tale Kalhor, Ahmad Sajedi, Hedieh Human and Robot Interaction Lab School of Electrical and Computer Engineering University of Tehran Tehran Iran School of Mathematics Statistics and Computer Science University of Tehran Tehran Iran

ISBN: (纸本)9798331511272

As smart homes become more prevalent in daily life, the ability to understand dynamic environments is essential which is increasingly dependent on AI systems. This study focuses on developing an intelligent algorithm which can navigate a robot through a kitchen, recognizing objects, and tracking their relocation. The kitchen was chosen as the testing ground due to its dynamic nature as objects are frequently moved, rearranged and replaced. Various techniques, such as SLAM feature-based tracking and deep learning-based object detection (e.g., Faster R-CNN), are commonly used for object tracking. Additionally, methods such as optical flow analysis and 3D reconstruction have also been used to track the relocation of objects. These approaches often face challenges when it comes to problems such as lighting variations and partial occlusions, where parts of the object are hidden in some frames but visible in others. The proposed method in this study leverages the YOLOv5 architecture, initialized with pre-trained weights and subsequently fine-tuned on a custom dataset. A novel method was developed, introducing a frame-scoring algorithm which calculates a score for each object based on its location and features within all frames. This scoring approach helps to identify changes by determining the best-associated frame for each object and comparing the results in each scene, overcoming limitations seen in other methods while maintaining simplicity in design. The experimental results demonstrate an accuracy of 97.72%, a precision of 95.83% and a recall of 96.84% for this algorithm, which highlights the efficacy of the model in detecting spatial changes. ©2024 IEEE.

关键词： Optical flows

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Kinematic-Sensitivity Indices for Double Gough-Stewart Platform

Kinematic-Sensitivity Indices for Double Gough-Stewart Platf...

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

作者： Niusha Zand Parsa Namazian Mehdi Tale Masouleh Human and Robot Interaction Lab. University of Theran Tehran Iran

ISBN: (数字)9798331529734

ISBN: (纸本)9798331529741

关键词： Jacobian matrices Sensitivity Translation Accuracy Shape Termination of employment Kinematics robot sensing systems End effectors Vectors

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Sound Source Localization in a 3D Multi-Surface Environment Using Learning-Based Methods

Sound Source Localization in a 3D Multi-Surface Environment ...

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

作者： Soroush Esfahanian Keyhan Rayati Mehdi Tale Masouleh Human and Robot Interaction Lab Electrical and Computer Engineering University of Tehran Tehran Iran

ISBN: (数字)9798331529734

ISBN: (纸本)9798331529741

关键词： Location awareness Learning systems Visualization Artificial neural networks Data models Sensors robots Random forests Microphones Context modeling

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Explorable Grasp Pose Detection for Two-Finger robot Handover

Explorable Grasp Pose Detection for Two-Finger Robot Handove...

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Iranian Conference on Electrical Engineering (ICEE)

作者： AliReza Beigy Mehdi Tale Masouleh Ahmad Kalhor Human and Robot Interaction Lab Electrical and Computer Engineering University of Tehran Tehran Iran

ISBN: (数字)9798350376340

ISBN: (纸本)9798350376357

The efficient generation of datasets for deep neural networks is crucial for enhancing their performance, especially in complex tasks like grasp pose detection in robotic grasp planning. Traditional methods often resort to random selection or rely on human-annotated datasets, making it challenging for networks to accurately learn graspable areas. For 3D grasp pose detection, an exhaustive search within an expansive space of points consumes substantial time and often overlooks an object's potential graspable regions. This paper introduces an algorithm capable of leveraging depth sensor information, particularly from devices like Kinect, to precisely determine the best grasp poses for object handovers. The proposed method, named Explorable Grasp Detection is based on the idea of exploitation and exploration, ensuring there is no redundant grasp detection within the same region. The results derived from DexNet's mesh objects dataset indicate remarkable computational efficiency, achieving grasp results in less than one second for various objects and maintaining a high success rate. Furthermore, error functions as geometry grasp quality metrics tailored for a two-finger gripper are incorporated to evaluate the grasp quality. One of the most notable contributions of this paper is the algorithm's ability to not only detect optimal grasping regions rapidly but also to serve as an instrumental tool for generating annotated datasets, thereby pushing forward the boundaries in robotic object manipulation techniques.

关键词： Point cloud compression Three-dimensional displays Instruments Grasping Handover robot sensing systems Search problems

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