Статті в журналах: "Deformable Object Manipulation"

1

Hirai, Shinichi. "Deformable Object Manipulation." Journal of the Robotics Society of Japan 16, no. 2 (1998): 136–39. http://dx.doi.org/10.7210/jrsj.16.136.

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2

Ono, Eiichi. "Deformable Object Manipulation. Fabric Manipulation." Journal of the Robotics Society of Japan 16, no. 2 (1998): 149–53. http://dx.doi.org/10.7210/jrsj.16.149.

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3

Hou, Yew Cheong, Khairul Salleh Mohamed Sahari, and Dickson Neoh Tze How. "A review on modeling of flexible deformable object for dexterous robotic manipulation." International Journal of Advanced Robotic Systems 16, no. 3 (May 1, 2019): 172988141984889. http://dx.doi.org/10.1177/1729881419848894.

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Анотація:

In this article, we present a review on the recent advancement in flexible deformable object modeling for dexterous manipulation in robotic system. Flexible deformable object is one of the most research topics in computer graphic, computer vision, and robotic literature. The deformable models are known as the construction of object with material parameters in virtual environment to describe the deformation behavior. Existing modeling techniques and different types of deformable model are described. Various approaches of deformable object modeling have been used in robotic recognition and manipulation in order to reduce the time and cost to obtain more accurate result. In robotic manipulation, object detection, classification, and recognition of deformable objects are always a challenging problem and required as a first step to imbue the robot to able handle these deformable objects. Furthermore, the dexterity of robot control is also another essential key in handling of deformable object which its manipulation strategies need to plan intelligently for each sequence process. We also discuss some deserving direction for further research based on most current contribution.

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4

Wakamatsu, Hidefumi, and Takahiro Wada. "Deformable Object Manipulation. Modeling of String Objects for Their Manipulation." Journal of the Robotics Society of Japan 16, no. 2 (1998): 145–48. http://dx.doi.org/10.7210/jrsj.16.145.

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5

Fujiura, Tateshi. "Deformable Object Manipulation. Manipulation of Agricultural Crops." Journal of the Robotics Society of Japan 16, no. 2 (1998): 168–71. http://dx.doi.org/10.7210/jrsj.16.168.

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6

Higashimori, Mitsuru, Ixchel Georgina Ramirez Alpizar, and Makoto Kaneko. "Modeling and Handling of Deformable Object by Nonprehensile Dynamic Manipulation." Abstracts of the international conference on advanced mechatronics : toward evolutionary fusion of IT and mechatronics : ICAM 2010.5 (2010): 427–32. http://dx.doi.org/10.1299/jsmeicam.2010.5.427.

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7

Hisada, Toshiaki. "Deformable Object Manipulation. Finite Element Modeling." Journal of the Robotics Society of Japan 16, no. 2 (1998): 140–44. http://dx.doi.org/10.7210/jrsj.16.140.

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8

Salleh, Khairul, Hiroaki Seki, Yoshitsugu Kamiya, and Masatoshi Hikizu. "Tracing Manipulation in Clothes Spreading by Robot Arms." Journal of Robotics and Mechatronics 18, no. 5 (October 20, 2006): 564–71. http://dx.doi.org/10.20965/jrm.2006.p0564.

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Edge tracing is important in manipulating deformable objects to reveal their original shape. In this paper, we propose a unique and improved tracing manipulation for towel spreading, an example of a deformable object using two robot arms with sensors-equipped grippers and a CCD camera. Tracing in this paper context involves tracing the towel’s edge. Robot arm movement is based on feedback from sensors and from images from the CCD camera. Our proposed tracing manipulation ensures that both corners grasped by robot are adjacent and not across, enabling the towel to be successfully spread. Experimental results from spreading rectangular towels with different thickness, stiffness, smoothness, and color using our improved tracing manipulation demonstrated that our proposal is also robust.

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9

Zaidi, Lazher, Juan Antonio Corrales Ramon, Laurent Sabourin, Belhassen Chedli Bouzgarrou, and Youcef Mezouar. "Grasp Planning Pipeline for Robust Manipulation of 3D Deformable Objects with Industrial Robotic Hand + Arm Systems." Applied Sciences 10, no. 23 (December 6, 2020): 8736. http://dx.doi.org/10.3390/app10238736.

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In the grasping and manipulation of 3D deformable objects by robotic hands, the physical contact constraints between the fingers and the object have to be considered in order to validate the robustness of the task. Nevertheless, previous works rarely establish contact interaction models based on these constraints that enable the precise control of forces and deformations during the grasping process. This paper considers all steps of the grasping process of deformable objects in order to implement a complete grasp planning pipeline by computing the initial contact points (pregrasp strategy), and later, the contact forces and local deformations of the contact regions while the fingers close over the grasped object (grasp strategy). The deformable object behavior is modeled using a nonlinear isotropic mass-spring system, which is able to produce potential deformation. By combining both models (the contact interaction and the object deformation) in a simulation process, a new grasp planning method is proposed in order to guarantee the stability of the 3D grasped deformable object. Experimental grasping experiments of several 3D deformable objects with a Barrett hand (3-fingered) and a 6-DOF industrial robotic arm are executed. Not only will the final stable grasp configuration of the hand + object system be obtained, but an arm + hand approaching strategy (pregrasp) will also be computed.

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10

A.Ayub, Muhammad, Rabiatul A.Jaafar, Amir Abdul Latif, and . "Tactile Sensor for Manipulation of Deformable Object." International Journal of Engineering & Technology 7, no. 4.27 (November 30, 2018): 101. http://dx.doi.org/10.14419/ijet.v7i4.27.22492.

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Анотація:

The variable physical property of deformable objects, which are very flexible, soft and viscoelastic, causes the design of reliable automated handling system relatively difficult. In fact, most of these objects tend to be handled manually during the handling process. Therefore, a new optical tactile sensor for an intelligent handling of the non-rigid materials is presented in this paper. Mathematical modelling and control algorithm are developed and the tactile sensor is calibrated in this research. Based on the results that have been recorded, the surface characterization with the respect to normal force applied to the object is attained. A gripper handling system is used to accommodate variable physical properties of the deformable materials, which are very flexible, soft and viscoelastic. In addition to that, the gripper needs to handle the materials with the minimum deformation so that less distortion, and higher accuracy of manipulation can be achieved. Efficient and accurate modelling of deformations is crucial for grasping analysis.

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11

Verleysen, Andreas, Thomas Holvoet, Remko Proesmans, Cedric Den Haese, and Francis wyffels. "Simpler Learning of Robotic Manipulation of Clothing by Utilizing DIY Smart Textile Technology." Applied Sciences 10, no. 12 (June 13, 2020): 4088. http://dx.doi.org/10.3390/app10124088.

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Deformable objects such as ropes, wires, and clothing are omnipresent in society and industry but are little researched in robotics research. This is due to the infinite amount of possible state configurations caused by the deformations of the deformable object. Engineered approaches try to cope with this by implementing highly complex operations in order to estimate the state of the deformable object. This complexity can be circumvented by utilizing learning-based approaches, such as reinforcement learning, which can deal with the intrinsic high-dimensional state space of deformable objects. However, the reward function in reinforcement learning needs to measure the state configuration of the highly deformable object. Vision-based reward functions are difficult to implement, given the high dimensionality of the state and complex dynamic behavior. In this work, we propose the consideration of concepts beyond vision and incorporate other modalities which can be extracted from deformable objects. By integrating tactile sensor cells into a textile piece, proprioceptive capabilities are gained that are valuable as they provide a reward function to a reinforcement learning agent. We demonstrate on a low-cost dual robotic arm setup that a physical agent can learn on a single CPU core to fold a rectangular patch of textile in the real world based on a learned reward function from tactile information.

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12

Garcia-Camacho, Irene, Julia Borras, Berk Calli, Adam Norton, and Guillem Alenya. "Household Cloth Object Set: Fostering Benchmarking in Deformable Object Manipulation." IEEE Robotics and Automation Letters 7, no. 3 (July 2022): 5866–73. http://dx.doi.org/10.1109/lra.2022.3158428.

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13

Sanchez, Jose, Juan-Antonio Corrales, Belhassen-Chedli Bouzgarrou, and Youcef Mezouar. "Robotic manipulation and sensing of deformable objects in domestic and industrial applications: a survey." International Journal of Robotics Research 37, no. 7 (June 2018): 688–716. http://dx.doi.org/10.1177/0278364918779698.

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We present a survey of recent work on robot manipulation and sensing of deformable objects, a field with relevant applications in diverse industries such as medicine (e.g. surgical assistance), food handling, manufacturing, and domestic chores (e.g. folding clothes). We classify the reviewed approaches into four categories based on the type of object they manipulate. Furthermore, within this object classification, we divide the approaches based on the particular task they perform on the deformable object. Finally, we conclude this survey with a discussion of the current state-of-the-art approaches and propose future directions within the proposed classification.

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14

Sahari, Khairul Salleh Mohamed, and Yew Cheong Hou. "3D Elastic Deformable Object Model for Robot Manipulation Purposes." Journal of Advanced Computational Intelligence and Intelligent Informatics 18, no. 3 (May 20, 2014): 375–82. http://dx.doi.org/10.20965/jaciii.2014.p0375.

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This paper presents a mass-spring model applied to the manipulation of an elastic deformable object for home service robot application. A system is also proposed that is used to fold a piece of rectangular cloth from a specific initial condition using a robot. The cloth is modeled as a three-dimensional object in a two-dimensional quadrangular mesh based on a massspring system, and its state is estimated using an explicit integration scheme that computes the particle position as a function of the internal and external forces acting on the elastic deformable object. The current state of the elastic deformable object under robot manipulation is tracked based on the trajectory of the mass points in the mass-spring system model in a self-developed simulator, which integrates a massspring model and a five-degree-of-freedom articulated robotic arm. To test the reliability of the model, the simulator is used to predict the best possible paths for using the robotic arm to fold a rectangular cloth into two. In the test, the state of the object is derived from the model and then compared with the results of a practical experiment. Based on the test, the error is found to be generally acceptable. Thus, this model can be used as an estimator for the vision-based tracking of the state of an elastic deformable object for manipulation by home service robots.

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15

Han, Tao, Xuan Zhao, Peigen Sun, and Jia Pan. "Robust shape estimation for 3D deformable object manipulation." Communications in Information and Systems 18, no. 2 (2018): 107–24. http://dx.doi.org/10.4310/cis.2018.v18.n2.a3.

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16

Chang, Peng, and Taşkın Padır. "Model-Based Manipulation of Linear Flexible Objects: Task Automation in Simulation and Real World." Machines 8, no. 3 (August 8, 2020): 46. http://dx.doi.org/10.3390/machines8030046.

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Manipulation of deformable objects is a desired skill in making robots ubiquitous in manufacturing, service, healthcare, and security. Common deformable objects (e.g., wires, clothes, bed sheets, etc.) are significantly more difficult to model than rigid objects. In this research, we contribute to the model-based manipulation of linear flexible objects such as cables. We propose a 3D geometric model of the linear flexible object that is subject to gravity and a physical model with multiple links connected by revolute joints and identified model parameters. These models enable task automation in manipulating linear flexible objects both in simulation and real world. To bridge the gap between simulation and real world and build a close-to-reality simulation of flexible objects, we propose a new strategy called Simulation-to-Real-to-Simulation (Sim2Real2Sim). We demonstrate the feasibility of our approach by completing the Plug Task used in the 2015 DARPA Robotics Challenge Finals both in simulation and real world, which involves unplugging a power cable from one socket and plugging it into another. Numerical experiments are implemented to validate our approach.

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17

Yew, Cheong Hou, Khairul Salleh Mohamed Sahari, and Cai Yin Gan. "Development of 3D Elastic Deformable Object Model for Robot Manipulation Purposes." Applied Mechanics and Materials 157-158 (February 2012): 1167–72. http://dx.doi.org/10.4028/www.scientific.net/amm.157-158.1167.

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This paper presents a mass-spring model applied in the manipulation of elastic deformable object for home service robot application. In this paper, we present a system used to fold a piece of rectangular cloth from a specific initial condition using robot. The cloth is modeled as a 3D object in a 2D quadrangular mesh based on a mass-spring system and its state is estimated using an explicit integration scheme that computes the particle position as a function of internal and external forces acting on the elastic deformable object. The state of the elastic deformable object under robot manipulation is currently tracked from the trajectory of the mass points in the mass-spring system model in a self developed simulator, which integrates a mass-spring model and a 5 DOF articulated robotic arm. To test the reliability of the model, the simulator is used to predict the best possible paths for the robotic arm to fold a rectangular cloth in two. In the test, the state of the object is derived from the model and then compared with practical experiment. Based on the test, the error is generally acceptable. Thus, this model can be used as an estimator for vision-based tracking on the state of an elastic deformable object for manipulation by home service robots.

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18

McConachie, Dale, Andrew Dobson, Mengyao Ruan, and Dmitry Berenson. "Manipulating deformable objects by interleaving prediction, planning, and control." International Journal of Robotics Research 39, no. 8 (June 19, 2020): 957–82. http://dx.doi.org/10.1177/0278364920918299.

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We present a framework for deformable object manipulation that interleaves planning and control, enabling complex manipulation tasks without relying on high-fidelity modeling or simulation. The key question we address is when should we use planning and when should we use control to achieve the task? Planners are designed to find paths through complex configuration spaces, but for highly underactuated systems, such as deformable objects, achieving a specific configuration is very difficult even with high-fidelity models. Conversely, controllers can be designed to achieve specific configurations, but they can be trapped in undesirable local minima owing to obstacles. Our approach consists of three components: (1) a global motion planner to generate gross motion of the deformable object; (2) a local controller for refinement of the configuration of the deformable object; and (3) a novel deadlock prediction algorithm to determine when to use planning versus control. By separating planning from control we are able to use different representations of the deformable object, reducing overall complexity and enabling efficient computation of motion. We provide a detailed proof of probabilistic completeness for our planner, which is valid despite the fact that our system is underactuated and we do not have a steering function. We then demonstrate that our framework is able to successfully perform several manipulation tasks with rope and cloth in simulation, which cannot be performed using either our controller or planner alone. These experiments suggest that our planner can generate paths efficiently, taking under a second on average to find a feasible path in three out of four scenarios. We also show that our framework is effective on a 16-degree-of-freedom physical robot, where reachability and dual-arm constraints make the planning more difficult.

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19

García-Rodríguez, Rodolfo, Victor Segovia-Palacios, Vicente Parra-Vega, and Marco Villalva-Lucio. "Dynamic optimal grasping of a circular object with gravity using robotic soft-fingertips." International Journal of Applied Mathematics and Computer Science 26, no. 2 (June 1, 2016): 309–23. http://dx.doi.org/10.1515/amcs-2016-0022.

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Abstract Object manipulation usually requires dexterity, encoded as the ability to roll, which is very difficult to achieve with robotic hands based on point contact models (subject to holonomic constraints). As an alternative for dexterous manipulation, deformable contact with hemispherical shape fingertips has been proposed to yield naturally a rolling constraint. It entails dexterity at the expense of dealing with normal and tangential forces, as well as more elaborated models and control schemes. Furthermore, the essential feature of the quality of grasp can be addressed with this type of robot hands, but it has been overlooked for deformable contact. In this paper, a passivity-based controller that considers an optimal grasping measure is proposed for robotic hands with hemispherical deformable fingertips, to manipulate circular dynamic objects. Optimal grasping that minimizes the contact wrenches is achieved through fingertip rolling until normal forces pass through the center of mass of the object, aligning the relative angle between these normal forces. The case of a circular object is developed in detail, though our proposal can be extended to objects with an arbitrary shape that admit a local decomposition by a circular curvature. Simulation and experimental results show convergence under various conditions, wherein rolling and tangent forces become instrumental to achieve such a quality of grasp.

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20

Keipour, Azarakhsh, Maryam Bandari, and Stefan Schaal. "Deformable One-Dimensional Object Detection for Routing and Manipulation." IEEE Robotics and Automation Letters 7, no. 2 (April 2022): 4329–36. http://dx.doi.org/10.1109/lra.2022.3146920.

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21

Hu, Zhe, Tao Han, Peigen Sun, Jia Pan, and Dinesh Manocha. "3-D Deformable Object Manipulation Using Deep Neural Networks." IEEE Robotics and Automation Letters 4, no. 4 (October 2019): 4255–61. http://dx.doi.org/10.1109/lra.2019.2930476.

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22

Yin, Hang, Anastasia Varava, and Danica Kragic. "Modeling, learning, perception, and control methods for deformable object manipulation." Science Robotics 6, no. 54 (May 26, 2021): eabd8803. http://dx.doi.org/10.1126/scirobotics.abd8803.

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Perceiving and handling deformable objects is an integral part of everyday life for humans. Automating tasks such as food handling, garment sorting, or assistive dressing requires open problems of modeling, perceiving, planning, and control to be solved. Recent advances in data-driven approaches, together with classical control and planning, can provide viable solutions to these open challenges. In addition, with the development of better simulation environments, we can generate and study scenarios that allow for benchmarking of various approaches and gain better understanding of what theoretical developments need to be made and how practical systems can be implemented and evaluated to provide flexible, scalable, and robust solutions. To this end, we survey more than 100 relevant studies in this area and use it as the basis to discuss open problems. We adopt a learning perspective to unify the discussion over analytical and data-driven approaches, addressing how to use and integrate model priors and task data in perceiving and manipulating a variety of deformable objects.

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23

Itakura, Osamu. "Deformable Object Manipulation. Manipulation of Paper Material. Ticket Handling in Station Business Machines." Journal of the Robotics Society of Japan 16, no. 2 (1998): 154–58. http://dx.doi.org/10.7210/jrsj.16.154.

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24

Cirillo, Andrea, Gianluca Laudante, and Salvatore Pirozzi. "Proximity Sensor for Thin Wire Recognition and Manipulation." Machines 9, no. 9 (September 3, 2021): 188. http://dx.doi.org/10.3390/machines9090188.

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Анотація:

In robotic grasping and manipulation, the knowledge of a precise object pose represents a key issue. The point acquires even more importance when the objects and, then, the grasping areas become smaller. This is the case of Deformable Linear Object manipulation application where the robot shall autonomously work with thin wires which pose and shape estimation could become difficult given the limited object size and possible occlusion conditions. In such applications, a vision-based system could not be enough to obtain accurate pose and shape estimation. In this work the authors propose a Time-of-Flight pre-touch sensor, integrated with a previously designed tactile sensor, for an accurate estimation of thin wire pose and shape. The paper presents the design and the characterization of the proposed sensor. Moreover, a specific object scanning and shape detection algorithm is presented. Experimental results support the proposed methodology, showing good performance. Hardware design and software applications are freely accessible to the reader.

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25

Cui, Zhenxi, Wanyu Ma, Jiewen Lai, Henry K. Chu, and Yi Guo. "Coupled Multiple Dynamic Movement Primitives Generalization for Deformable Object Manipulation." IEEE Robotics and Automation Letters 7, no. 2 (April 2022): 5381–88. http://dx.doi.org/10.1109/lra.2022.3156656.

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26

Costanzo, Marco, Giuseppe De Maria, Ciro Natale, and Salvatore Pirozzi. "Design of a Force/Tactile Sensor for Robotic Grippers." Proceedings 15, no. 1 (July 25, 2019): 31. http://dx.doi.org/10.3390/proceedings2019015031.

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This paper presents the design of a new force/tactile sensor for robotic applications. The sensor is suitably designed to provide the robotic grasping device with a sensory system mimicking the human sense of touch, namely, a device sensitive to contact forces, object slip and object geometry. This type of perception information is of paramount importance not only in dexterous manipulation but even in simple grasping task, especially when objects are fragile and deformable, such that only a minimum amount of grasping force can be applied to hold the object without damaging it.

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27

Annaswamy, A. M., and D. Seto. "Object Manipulation Using Compliant Fingerpads: Modeling and Control." Journal of Dynamic Systems, Measurement, and Control 115, no. 4 (December 1, 1993): 638–48. http://dx.doi.org/10.1115/1.2899191.

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Анотація:

Current industrial robots are often required to perform tasks requiring mechanical interactions with their environment. For tasks that require grasping and manipulation of unknown objects, it is crucial for the robot end-effector to be compliant to increase grasp stability and manipulability. The dynamic interactions that occur between such compliant end-effectors and deformable objects that are being manipulated can be described by a class of nonlinear systems. In this paper, we determine algorithms for grasping and manipulation of these objects by using adaptive feedback techniques. Methods for control and adaptive control of the underlying nonlinear system are described. It is shown that although standard geometric techniques for exact feedback linearization techniques are inadequate, yet globally stable adaptive control algorithms can be determined by making use of the stability characteristics of the underlying nonlinear dynamics.

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28

Ibarra-Zannatha, Juan Manuel, Claudia Marmolejo-Rivas, Manuel Ferre-Pérez, Rafael Aracil-Santonja, and Salvador Cobos-Guzmán. "Haptic Manipulation of Deformable Objects in Hybrid Bilateral Teleoperation System." Applied Bionics and Biomechanics 4, no. 4 (2007): 157–68. http://dx.doi.org/10.1155/2007/181272.

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The aim of this work is the integration of a virtual environment containing a deformable object, manipulated by an open kinematical chain virtual slave robot, to a bilateral teleoperation scheme based on a real haptic device. The virtual environment of this hybrid bilateral teleoperation system combines collision detection algorithms, dynamical, kinematical and geometrical models with a position–position and/or force–position bilateral control algorithm, to produce on the operator side the reflected forces corresponding to the virtual mechanical interactions, through a haptic device. Contact teleoperation task over the virtual environment with a flexible object is implemented and analysed.

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29

Antonova, Rika, Jingyun Yang, Priya Sundaresan, Dieter Fox, Fabio Ramos, and Jeannette Bohg. "A Bayesian Treatment of Real-to-Sim for Deformable Object Manipulation." IEEE Robotics and Automation Letters 7, no. 3 (July 2022): 5819–26. http://dx.doi.org/10.1109/lra.2022.3157377.

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30

Hu, Zhe, Peigen Sun, and Jia Pan. "Three-Dimensional Deformable Object Manipulation Using Fast Online Gaussian Process Regression." IEEE Robotics and Automation Letters 3, no. 2 (April 2018): 979–86. http://dx.doi.org/10.1109/lra.2018.2793339.

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31

Mcconachie, Dale, and Dmitry Berenson. "Estimating Model Utility for Deformable Object Manipulation Using Multiarmed Bandit Methods." IEEE Transactions on Automation Science and Engineering 15, no. 3 (July 2018): 967–79. http://dx.doi.org/10.1109/tase.2018.2822669.

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32

Cirillo, Andrea, Gianluca Laudante, and Salvatore Pirozzi. "Tactile Sensor Data Interpretation for Estimation of Wire Features." Electronics 10, no. 12 (June 18, 2021): 1458. http://dx.doi.org/10.3390/electronics10121458.

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Анотація:

At present, the tactile perception is essential for robotic applications when performing complex manipulation tasks, e.g., grasping objects of different shapes and sizes, distinguishing between different textures, and avoiding slips by grasping an object with a minimal force. Considering Deformable Linear Object manipulation applications, this paper presents an efficient and straightforward method to allow robots to autonomously work with thin objects, e.g., wires, and to recognize their features, i.e., diameter, by relying on tactile sensors developed by the authors. The method, based on machine learning algorithms, is described in-depth in the paper to make it easily reproducible by the readers. Experimental tests show the effectiveness of the approach that is able to properly recognize the considered object’s features with a recognition rate up to 99.9%. Moreover, a pick and place task, which uses the method to classify and organize a set of wires by diameter, is presented.

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33

Essahbi, Nabil, Belhassen Chedli Bouzgarrou, and Grigore Gogu. "Soft Material Modeling for Robotic Manipulation." Applied Mechanics and Materials 162 (March 2012): 184–93. http://dx.doi.org/10.4028/www.scientific.net/amm.162.184.

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Анотація:

This article presents a new approach of soft material modeling for robotic manipulation which combines physical modeling and tracking of a deformable object. We discuss the construction of geometrical models using MRI system and how to overcome the problem of variability. The physical model focuses on meat/muscles deformation. We introduce the principal criteria for choosing most appropriate models and discuss two modeling methods which suit our problem: mass spring model and tensor mass model. The introduction of anisotropy in these models allows results to be more realistic but evolves an increasing of computing time.

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34

Furuya, Hiroaki, and Singo Nakahara. "Deformable Object Manipulation. Automated Equipment to Assemble Connectors for Telecommunication Metallic Cables." Journal of the Robotics Society of Japan 16, no. 2 (1998): 163–67. http://dx.doi.org/10.7210/jrsj.16.163.

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35

Boonvisut, Pasu, and M. Cenk Çavuşoğlu. "Identification and active exploration of deformable object boundary constraints through robotic manipulation." International Journal of Robotics Research 33, no. 11 (August 22, 2014): 1446–61. http://dx.doi.org/10.1177/0278364914536939.

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36

Tsurumine, Yoshihisa, Yunduan Cui, Kimitoshi Yamazaki, and Takamitsu Matsubara. "Imitation Learning of Deformable Object Manipulation with Entropy-maximizing Dynamic Policy Programming." Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) 2019 (2019): 1P2—A11. http://dx.doi.org/10.1299/jsmermd.2019.1p2-a11.

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37

Nakagaki, Hirofumi. "Deformable Object Manipulation. Insertion Task of a Flexible Beam or a Flexible Wire." Journal of the Robotics Society of Japan 16, no. 2 (1998): 159–62. http://dx.doi.org/10.7210/jrsj.16.159.

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38

Mitrano, P., D. McConachie, and D. Berenson. "Learning where to trust unreliable models in an unstructured world for deformable object manipulation." Science Robotics 6, no. 54 (May 19, 2021): eabd8170. http://dx.doi.org/10.1126/scirobotics.abd8170.

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The world outside our laboratories seldom conforms to the assumptions of our models. This is especially true for dynamics models used in control and motion planning for complex high–degree of freedom systems like deformable objects. We must develop better models, but we must also consider that, no matter how powerful our simulators or how big our datasets, our models will sometimes be wrong. What is more, estimating how wrong models are can be difficult, because methods that predict uncertainty distributions based on training data do not account for unseen scenarios. To deploy robots in unstructured environments, we must address two key questions: When should we trust a model and what do we do if the robot is in a state where the model is unreliable. We tackle these questions in the context of planning for manipulating rope-like objects in clutter. Here, we report an approach that learns a model in an unconstrained setting and then learns a classifier to predict where that model is valid, given a limited dataset of rope-constraint interactions. We also propose a way to recover from states where our model prediction is unreliable. Our method statistically significantly outperforms learning a dynamics function and trusting it everywhere. We further demonstrate the practicality of our method on real-world mock-ups of several domestic and automotive tasks.

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39

Ramirez-Alpizar, Ixchel G., Mitsuru Higashimori, Makoto Kaneko, Chia-Hung Dylan Tsai, and Imin Kao. "Dynamic Nonprehensile Manipulation for Rotating a Thin Deformable Object: An Analogy to Bipedal Gaits." IEEE Transactions on Robotics 28, no. 3 (June 2012): 607–18. http://dx.doi.org/10.1109/tro.2011.2181098.

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40

Huang, J., I. Todo, and Y. Ogura. "Control for Robot Manipulation of a Deformable Object Using Visual and Force/Torqe Information." Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) 2004 (2004): 131. http://dx.doi.org/10.1299/jsmermd.2004.131_1.

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41

Matsuno, T., D. Tamaki, F. Arai, and T. Fukuda. "Manipulation of deformable linear objects using knot invariants to classify the object condition based on image sensor information." IEEE/ASME Transactions on Mechatronics 11, no. 4 (August 2006): 401–8. http://dx.doi.org/10.1109/tmech.2006.878557.

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42

Lee, Robert, Masashi Hamaya, Takayuki Murooka, Yoshihisa Ijiri, and Peter Corke. "Sample-Efficient Learning of Deformable Linear Object Manipulation in the Real World Through Self-Supervision." IEEE Robotics and Automation Letters 7, no. 1 (January 2022): 573–80. http://dx.doi.org/10.1109/lra.2021.3130377.

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43

Das, Jadav, and Nilanjan Sarkar. "Passivity-based target manipulation inside a deformable object by a robotic system with noncollocated feedback." Advanced Robotics 27, no. 11 (August 2013): 861–75. http://dx.doi.org/10.1080/01691864.2013.791657.

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44

NOZAKI, Kyoto, Yuichiro MATSUURA, and Kimitoshi YAMAZAKI. "Manipulation Planning of Wiring a Cable with Connector Considering Shape Transition of Deformable Linear Object." Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) 2022 (2022): 2A1—N07. http://dx.doi.org/10.1299/jsmermd.2022.2a1-n07.

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45

Arimoto, Suguru, Zoe Doulgeri, Pham Thuc Anh Nguyen, and John Fasoulas. "Stable pinching by a pair of robot fingers with soft tips under the effect of gravity." Robotica 20, no. 3 (May 2002): 241–49. http://dx.doi.org/10.1017/s0263574701003976.

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This paper analyses lumped-parameter dynamics of a pair of robot fingers with soft and deformable tips pinching a rigid object under the effect of a gravity force. The dynamics of the system in which area contacts between the finger-tips and the surfaces of the object arise are compared with those of a pair of rigid robot fingers with rigid contacts with an object, with or without effect of the gravity. It is then shown that there exists a sensory feedback from measurement of finger joint angles and the rotational angle of the object to command inputs to joint actuators, and this feedback connection from sensing to action realizes secure grasping of the object in a dynamic sense and regulation of the object posture. It is further shown that there are various types of other feedback connections from sensing to action, which can be used in combination of feedback signals for stable grasping and posture control of the object for realizing sophisticated object manipulation.

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46

Tanaka, Daisuke, Solvi Arnold, and Kimitoshi Yamazaki. "Disruption-Resistant Deformable Object Manipulation on Basis of Online Shape Estimation and Prediction-Driven Trajectory Correction." IEEE Robotics and Automation Letters 6, no. 2 (April 2021): 3809–16. http://dx.doi.org/10.1109/lra.2021.3060679.

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47

Peng, Hanmin, Ting Mao, and Xiaolong Lu. "A small legged deformable robot with multi-mode motion." Journal of Intelligent Material Systems and Structures 31, no. 5 (January 10, 2020): 704–18. http://dx.doi.org/10.1177/1045389x19898251.

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To realize walking and working in complex terrain or narrow space, a small legged robot with a mass of 5.6 g and a size of 70 mm × 60 mm × 30 mm is proposed. Piezoelectric and shape memory alloy actuators are combined for fast response and high flexibility. Six piezoelectric bimorphs serving as driving legs realize fast linear and turning motions in the forward and backward directions. Four shape memory alloy springs are excited for raising and dropping different legs to generate multiple motion modes. A dynamic model is built to guarantee the lifting motions of the designated legs. The experimental results show that it achieves linear moving and turning speeds as fast as 24.8 and 16.5 cm/s, respectively, whereas its startup time is only 0.1 s. Moreover, this robot lifts different legs up to 1 cm high with response time of 6, 8, and 6 s under the current of 1.5 A, respectively, which can recover to initial status. Hence, this robot is capable of fulfilling manipulation tasks, such as terrain detection, material transportation, obstacle crossing, and object capturing, thanks to the characteristics of small size, simple structure, good flexibility, and multi-functional locomotion.

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48

KANOU, Kazunori, and Fumiaki OSAWA. "1P1-G03 Deformable Sheet Object Manipulation Using Outline Operation : Shape reconstruction by information of outline(Robots for Works)." Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) 2012 (2012): _1P1—G03_1—_1P1—G03_2. http://dx.doi.org/10.1299/jsmermd.2012._1p1-g03_1.

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49

Verleysen, Andreas, Matthijs Biondina, and Francis wyffels. "Video dataset of human demonstrations of folding clothing for robotic folding." International Journal of Robotics Research 39, no. 9 (July 10, 2020): 1031–36. http://dx.doi.org/10.1177/0278364920940408.

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General-purpose clothes-folding robots do not yet exist owing to the deformable nature of textiles, making it hard to engineer manipulation pipelines or learn this task. In order to accelerate research for the learning of the robotic clothes-folding task, we introduce a video dataset of human folding demonstrations. In total, we provide 8.5 hours of demonstrations from multiple perspectives leading to 1,000 folding samples of different types of textiles. The demonstrations are recorded in multiple public places, in different conditions with a diverse set of people. Our dataset consists of anonymized RGB images, depth frames, skeleton keypoint trajectories, and object labels. In this article, we describe our recording setup, the data format, and utility scripts, which can be accessed at https://adverley.github.io/folding-demonstrations .

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50

Mattar, Ebrahim. "A Neurofuzzy Knowledge Based Architecture for Robotic Hand Manipulation Forces Learning." International Journal of Intelligent Mechatronics and Robotics 3, no. 2 (April 2013): 16–38. http://dx.doi.org/10.4018/ijimr.2013040102.

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Optimal distribution of forces for manipulation by a robot hand, is a hard computational issue, specifically once a whole hand grasp is needed. It becomes a complicated issue, once a robotic hand is equipped with human like deformable sensory touching materials. For computing optimal set of manipulation forces, grip transform and inverse hand Jacobian play major roles for such purposes. This manuscript is discussing a Neurofuzzy learning technique for learning optimal force distribution by a dextrous hand. For learning purposes, optimal set of forces patterns were gathered in advanced using optimization formulation technique. After that, to let a Neurofuzzy system to learn the nonlinear kinematics-dynamics relations needed for force distribution. This is done by considering the computational requirements for the inverse hand Jacobian, in addition to the interaction between hand fingers and the object. Training patterns clustering, and generation of the fuzzy initial memberships, and updated shape of memberships, are considered as vital information to build upon for more reasoning of fuzzy interrelation. The technique is novel in a sense, that the adopted Neurofuzzy architecture was transparent in terms of revealing the learned hand optimal forces if then rules.

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