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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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Real-Time Facial Expression Recognition using Facial Landmarks and Neural Networks 12

Real-Time Facial Expression Recognition using Facial Landmar...

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12th Iranian/2nd International Conference on Machine Vision and Image Processing, MVIP 2022

作者： Haghpanah, Mohammad A. Saeedizade, Ehsan Masouleh, Mehdi Tale Kalhor, Ahmad University of Tehran Human and Robot Interaction Lab School of Electrical and Computer Engineering Tehran Iran University of Nevada Department of Computer Science and Engineering Reno United States

ISBN: (纸本)9781665412162

This paper presents a lightweight algorithm for feature extraction, classification of seven different emotions, and facial expression recognition in a real-time manner based on static images of the human face. In this regard, a Multi-Layer Perceptron (MLP) neural network is trained based on the foregoing algorithm. In order to classify human faces, first, some pre-processing is applied to the input image, which can localize and cut out faces from it. In the next step, a facial landmark detection library is used, which can detect the landmarks of each face. Then, the human face is split into upper and lower faces, which enables the extraction of the desired features from each part. In the proposed model, both geometric and texture-based feature types are taken into account. After the feature extraction phase, a normalized vector of features is created. A 3-layer MLP is trained using these feature vectors, leading to 96% accuracy on the test set. © 2022 IEEE.

关键词： Emotion Recognition

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Road Sign Classification Using Transfer Learning and Pre-trained CNN Models 1

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1st International Conference on Artificial Intelligence and Smart Vehicles, ICAISV 2023

作者： Hosseini, Seyed Hossein Ghaderi, Foad Moshiri, Behzad Norouzi, Mojtaba Human Computer Interaction Lab Faculty of Electrical and Computer Engineering Tarbiat Modares University Tehran Iran School of Electrical and Computer Engineering University of Tehran Tehran Iran School of ECE University of Science and Technology Tehran Iran

ISBN: (数字)9783031437632

ISBN: (纸本)9783031437625

In this paper, we propose a transfer learning-based approach for road sign classification using pre-trained CNN models. We evaluate the performance of our fine-tuned VGG-16, VGG-19, ResNet50 and EfficientNetB0 models on the German Traffic Sign Recognition Benchmark (GTSRB) test dataset. Our work makes several contributions, including the utilization of transfer learning with pre-trained CNN models, the integration of augmentation techniques, and a comprehensive evaluation of multiple state-of-the-art architectures on the road sign classification task. Our experimental results show that the proposed approach achieves an average accuracy of 99.2% on the GTSRB test dataset, outperforming existing state-of-the-art approaches. The results confirm that transfer learning and pre-trained models can significantly improve the accuracy of road sign classification, even with a relatively small dataset. Our study contributes to the field of intelligent transportation systems by providing an efficient and accurate method for road sign classification. We believe that our approach can be easily extended to other computer vision tasks and applied to real-world scenarios. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Autonomous vehicles

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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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Experimental Study on Autonomous Food Packaging with Delta Parallel robot and Two Fingered Gripper

Experimental Study on Autonomous Food Packaging with Delta P...

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Iranian Conference on electrical engineering (ICEE)

作者： Mona Mohades Mojtahedi Arvin Mohammadi Mehdi Tale Masouleh Human and Robot Interaction Laboratory School of Electrical and Computer Engineering University of Tehran Tehran Iran Human and Robot Interaction Laboratory School of Mechanical Engineering University of Tehran Tehran Iran

ISBN: (数字)9798350376340

ISBN: (纸本)9798350376357

In response to the increasing supply and demand in the food industry, automated food packaging has emerged as a substitute for the manual lab.r that was previously required. The surge in automation is evident, with robotics playing a pivotal role in tasks ranging from sorting and packaging to lab.ling, and notably, pick-and-place operations which is the specific focus of this study. Leveraging robots for these tasks not only improves precision and speed but also takes packaging hygiene into account, resulting in a substantial cost reduction and heightened efficiency. Delta Parallel robots are among the robotic systems well-suited for high-speed pick-and-place tasks. In this experimental study, the focus is on packaging several scattered chocolates, where both the container box and the chocolates will be randomly positioned and oriented. This is achieved by employing a Delta Parallel robot paired with a two-fingered gripper, designed to incur minimal damage to the product when compared to other types of end-effectors. The positioning of the box and products will be determined through a classical image processing approach, specifically Edge Detection and Hough Transform, implemented using the OpenCV Python library. Following identification, the objects are localized through camera-robot calibration, and the resulting coordinates are transmitted to the robot as target points. Extensive testing has demonstrated that this study, employing visual perception for pick-and-place tasks, achieved a success rate of 82%. This study showcases the potential of automating packaging tasks in the food industry through the integration of robotic systems and artificial intelligence. The approach proves effective across diverse tasks, encompassing the packaging of variously shaped food items and drugs, applicable across different industries.

关键词： Parallel robots Accuracy Service robots Shape Image color analysis robot kinematics Food industry

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Deep Learning-Based Imitation of human Actions for Autonomous Pick-and-Place Tasks

Deep Learning-Based Imitation of Human Actions for Autonomou...

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Iranian Conference on electrical engineering (ICEE)

作者： Anushe Saadati Mehdi Tale Masouleh Ahmad Kalhor Human and Robot Interaction Laboratory School of Electrical and Computer Engineering University of Tehran Tehran Iran

ISBN: (数字)9798350376340

ISBN: (纸本)9798350376357

In robotics, attempts have been made to imitate movements through learning from demonstrations. There are two types of demonstration-based learning. Expert-based learning is expensive and time-consuming since it necessitates a paired observation-action procedure. Conversely, visual-based learning is more practical to collect and makes robots more adaptable to many kinds of tasks. But this problem still needs to be answered completely, and it's still a difficult assignment. This paper presents a comprehensive approach to replicating human actions in robots for object manipulation. As the main contribution of this paper, This research employs a deep learning methodology to allow robots to imitate human actions in grasping objects, utilizing human movement as a reference. A real dataset with 718 images and 142 videos has been collected in this paper. then The model is trained for object detection and object segmentation. To imitate human actions, a video of a person completing a task is recorded first. Following the subject's movements in the video allows for the extraction of the motion path. The robot then proceeds along the path that was extracted. The DPR impressively demonstrates the practical application, showcasing its capacity to imitate and execute human actions.

关键词： Deep learning Accuracy Tracking Video sequences Object detection Grasping Real-time systems

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Developing a Reinforcement Learning-Based Actor-Critic Neural Network Controller for General Form of Serial Chain robots with Torque Constraints

SSRN

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SSRN 2024年

作者： Bahrami, Vahid Masouleh, Mehdi Tale Kalhor, Ahmad Human and Robot Interaction Laboratory School of Electrical and Computer Engineering University of Tehran Tehran Iran

This paper uses reinforcement learning techniques to introduce a novel approach to the controller design for a wide range of serial chain robots and cable-driven serial chain robots. The method addresses the regulation problem by designing a regulator and steady-state torques without requiring knowledge of the Coriolis vector, a complex part of the robot's dynamic model. The approach relies on Denavit-Hartenberg parameters to determine regulator and desired steady-state torques, using an actor-critic neural network for adaptation and a multi-layer perceptron neural network for desired steady-state torque. Stability is ensured through Lyapunov functions, with only the mass matrix and gravity vector required. The proposed control scheme offers adaptability, robustness, scalab.lity, and optimization features compared to traditional controllers. Simulations on a 4-degrees-of-freedom cable-driven serial chain robot validate the proposed control method's effectiveness. A comparative benchmark is performed, demonstrating that the reinforcement learning controller outperforms other controllers in terms of regulation error, reducing it by at least 17.14\% compared to deep deterministic policy gradient and robust integral of the sign of the error methods. © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

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From Bricks to Bots: Automated Collision-Aware Sequence Planning for LEGO Reconstruction with a Two-Finger Gripper

From Bricks to Bots: Automated Collision-Aware Sequence Plan...

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International Conference on Control, Instrumentation, and Automation

作者： Arman Barghi Navid Pasiar Mehdi Tale Masouleh Ahmad Kalhor Human and Robot Interaction Laboratory School of Electrical and Computer Engineering University of Tehran Tehran Iran

ISBN: (数字)9798331509972

ISBN: (纸本)9798331509989

An automated, vision-guided assembly method is proposed for reconstructing LEGO arrangements using a 3 DOF Delta Parallel robot with a two-finger gripper. Assembly sequence planning, an NP-complete problem, focuses on determining the optimal sequence of bricks for constructing a target structure. The complexity increases when robotic con-straints-such as avoiding collisions between the gripper and the partially constructed structure-are integrated into the planning. To address this, the Arrangement Planning Process is introduced, combining target arrangement identification with collision-aware sequence planning. This process includes precise detection techniques that merge instance segmentation with stud detection. Notable innovations include an assembly-by-disassembly algorithm for efficient planning and experimental techniques such as the Safe Approach and the Wiggling Technique for accurate brick placement. By implementing a homing method, positioning accuracy improved approximately twofold compared to previous work. The results demonstrate the feasibility and precision of the proposed method in automated LEGO reconstruction.

关键词： Instance segmentation Parallel robots Technological innovation Accuracy Three-dimensional displays Service robots Robustness Planning Grippers Assembly

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Autonomous Rearrangement Planning Using Object Similarity Learning

Autonomous Rearrangement Planning Using Object Similarity Le...

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

作者： Parmida Jabari Zeynab Ezzati Babi Hamed Ghasemi Mehdi Tale Masouleh Ahmad Kalhor Human and Robot Interaction Laboratory School of Electrical and Computer Engineering University of Tehran Tehran Iran

ISBN: (数字)9798331529734

ISBN: (纸本)9798331529741

This study investigates the integration of artificial intelligence in robotics assembly systems, focusing on enhancing flexibility and adaptability in dynamic environments where the robot might not be given the precise objects of the intended construction. To this end, a developed approach is presented which is based on object detection, similarity assessment, and sequence planning for autonomous construction tasks. The system utilizes a YOLOv8 model for object detection and a Siamese network architecture, which facilitates object replacement. It processes two input images, one representing the target structure and another showing the availab.e objects, extracting attributes such as bounding boxes, centers, and orientations. An exact object-matching step is performed before a two-stage similarity assessment. The two-stage similarity assessment prioritizes single-object matches and employs a multi-object similarity network only when necessary. This multi-stage approach is designed to maintain high similarity to the intended design while minimizing possible object shortcomings. During the construction phase, motion planning is performed using a 3-degrees-of-freedom Delta parallel robot, and a 2-finger gripper is utilized for robotics grasping. The results of this study, conducted on wooden bricks of varying shapes and sizes, demonstrate the effectiveness of the YOLOv8 model for object detection, achieving over 80% accuracy across 18 classes with a classification loss of approximately 0.25. Additionally, the Siamese network performed robustly, with a loss of less than 0.01 for both single and multiple object comparisons.

关键词： robotic assembly Parallel robots Automation Autonomous systems robot vision systems Warehousing Object detection Cameras Planning Grippers

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Model-Free Dynamic Control of Five-bar Mechanism via Subchain Assembly and Reinforcement Learning

Model-Free Dynamic Control of Five-bar Mechanism via Subchai...

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

作者： Soheil Sedghi Mehdi Tale Masouleh Ahmad Kalhor Human and Robot Interaction Laboratory School of Electrical and Computer Engineering University of Tehran Tehran Iran

ISBN: (数字)9798331529734

ISBN: (纸本)9798331529741

Parallel robots are versatile but challenging to control due to their complex dynamics and the need for precise modeling. This research presents a novel approach to modular control of parallel robots, utilizing reinforcement learning to dynamically assemble and control complex arbitrary systems. By treating each sub chain of a parallel robot as an individual agent, the proposed method offers a flexible and adaptable framework for various control tasks. The study compares the learning performance of different RL algorithms and proposed decision modeling on the tracking control of a five-bar robot with a 55 cm link length, aiming to achieve an error of less than 5 cm in the PyBullet simulator. In single-agent reinforcement learning, three algorithms (DDPG, PPO, and SAC) were employed, with PPO and SAC effectively solving the problem. In multi-agent reinforcement learning, two algorithms (FACMAC and MAPPO) were utilized. While MAPPO successfully learned the problem, FACMAC exhibited difficulties in grasping the problem. The results obtained from comparing the proposed RL algorithms indicated that single-agent RL learns the control problem better and faster than multi-agent reinforcement learning. In short, the paper focuses on formulating an RL solution which can effectively control parallel robots and can potentially be used for pre-training subchains.

关键词： Training Parallel robots Adaptation models Sensitivity Mechatronics Heuristic algorithms Transfer learning Neural networks Training data Reinforcement learning

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