Robotic Manipulation of Elongated and Elastic Objects

Kicki, Piotr; Bednarek, Michał; Walas, Krzysztof

doi:10.23919/spa.2019.8936834

Cited by 5 publications

(3 citation statements)

References 52 publications

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“…Estimation-The method for the object stiffness estimation from the force sensor readings was proposed in [17]. Authors used small optical force sensors mounted on the fingertips, a known kinematic model of the robotic hand and a vision system to calculate the stiffness based on the force and displacement readings.…”

Section: Measuring and Estimating A Stiffnessmentioning

confidence: 99%

Gaining a Sense of Touch Object Stiffness Estimation Using a Soft Gripper and Neural Networks

et al. 2021

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Soft grippers are gaining significant attention in the manipulation of elastic objects, where it is required to handle soft and unstructured objects, which are vulnerable to deformations. The crucial problem is to estimate the physical parameters of a squeezed object to adjust the manipulation procedure, which poses a significant challenge. The research on physical parameters estimation using deep learning algorithms on measurements from direct interaction with objects using robotic grippers is scarce. In our work, we proposed a trainable system which performs the regression of an object stiffness coefficient from the signals registered during the interaction of the gripper with the object. First, using the physics simulation environment, we performed extensive experiments to validate our approach. Afterwards, we prepared a system that works in a real-world scenario with real data. Our learned system can reliably estimate the stiffness of an object, using the Yale OpenHand soft gripper, based on readings from Inertial Measurement Units (IMUs) attached to the fingers of the gripper. Additionally, during the experiments, we prepared three datasets of IMU readings gathered while squeezing the objects—two created in the simulation environment and one composed of real data. The dataset is the contribution to the community providing the way for developing and validating new approaches in the growing field of soft manipulation.

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Section: Measuring and Estimating A Stiffnessmentioning

confidence: 99%

Gaining a Sense of Touch Object Stiffness Estimation Using a Soft Gripper and Neural Networks

et al. 2021

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“…The system utilises both haptic and vision sensors to execute the task. In [ 19 ], the authors also used both haptic and visual feedback to manipulate the deformable linear object bimanually. Whereas, in [ 20 ] a vision-based only system was used to perform deformable object manipulation.…”

Section: Related Workmentioning

confidence: 99%

Tell Me, What Do You See?—Interpretable Classification of Wiring Harness Branches with Deep Neural Networks

Kicki

Bednarek

Lembicz³

et al. 2021

Sensors

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In the context of the robotisation of industrial operations related to manipulating deformable linear objects, there is a need for sophisticated machine vision systems, which could classify the wiring harness branches and provide information on where to put them in the assembly process. However, industrial applications require the interpretability of the machine learning system predictions, as the user wants to know the underlying reason for the decision made by the system. We propose several different neural network architectures that are tested on our novel dataset to address this issue. We conducted various experiments to assess the influence of modality, data fusion type, and the impact of data augmentation and pretraining. The outcome of the network is evaluated in terms of the performance and is also equipped with saliency maps, which allow the user to gain in-depth insight into the classifier’s operation, including a way of explaining the responses of the deep neural network and making system predictions interpretable by humans.

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“…The real-world objects were classified in the set of haptic adjectives in the multi-label fashion based on haptic signals from BioTac sensors [28] and images. The method for object stiffness estimation was proposed in [13]. Authors used small optical force sensors mounted on the fingertips, a known kinematic model of the robotic hand and a vision system to calculate the stiffness based on the force and displacement readings.…”

Section: A Stiffness Measurements and Estimation Techniquesmentioning

confidence: 99%

Gaining a Sense of Touch. Physical Parameters Estimation using a Soft Gripper and Neural Networks

Bednarek,

Kicki,

Bednarek

et al. 2020

Preprint

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Soft grippers are gaining significant attention in the manipulation of elastic objects, where it is required to handle soft and unstructured objects which are vulnerable to deformations. A crucial problem is to estimate the physical parameters of a squeezed object to adjust the manipulation procedure, which is considered as a significant challenge. To the best of the authors' knowledge, there is not enough research on physical parameters estimation using deep learning algorithms on measurements from direct interaction with objects using robotic grippers. In our work, we proposed a trainable system for the regression of a stiffness coefficient and provided extensive experiments using the physics simulator environment. Moreover, we prepared the application that works in the real-world scenario. Our system can reliably estimate the stiffness of an object using the Yale OpenHand soft gripper based on readings from Inertial Measurement Units (IMUs) attached to its fingers. Additionally, during the experiments, we prepared three datasets of signals gathered while squeezing objects -two created in the simulation environment and one composed of real data.

show abstract

Robotic Manipulation of Elongated and Elastic Objects

Cited by 5 publications

References 52 publications

Gaining a Sense of Touch Object Stiffness Estimation Using a Soft Gripper and Neural Networks

Gaining a Sense of Touch Object Stiffness Estimation Using a Soft Gripper and Neural Networks

Tell Me, What Do You See?—Interpretable Classification of Wiring Harness Branches with Deep Neural Networks

Gaining a Sense of Touch. Physical Parameters Estimation using a Soft Gripper and Neural Networks

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