1,180 research outputs found
REMODEL. WP5. Cable Manipulation Planning Execution And Interactive Perception. T5_2. Cable Grasping. Identification and Grasping of Deformable Objects. v0
No full textThe dataset contains the source code and pointclouds utilized during the experiments carried out concerning the optimal identification of grasping poses in clothes, associated to the related publication: A. Caporali and G. Palli, "Pointcloud-based Identification of Optimal Grasping Poses for Cloth-like Deformable Objects," 2020 25th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), Vienna, Austria, 2020, pp. 581-586, doi: 10.1109/ETFA46521.2020.9211879
Costantino Mortati a Macerata: opera e scritti minori. La Costituzione materiale nella prospettiva di una codificazione dei principii di regime
No full textIl presente lavoro si propone di tracciare un quadrdell’esperienza accademica
e degli scritti minori di Costantino Mortati durante il periodo in
cui visse ed operò presso l’Università di Macerata, in special modo come
Rettore. Per quel che concerne l’attività scientifica di Mortati si sono esaminati
tutti gli scritti minori riferibili al periodo maceratese, allo scopo di
delineare la concezione definitiva che Mortati aveva intorno allo Stato, ed
in particolare allo Stato fascista, visto che dopo il crollo del Regime nel luglio
1943 il chiaro autore non scriverà più nulla in proposito, neppure con
intenti storico-ricostruttivi. Prospettiva in cui assume un peculiare rilievo lo
scritto dedicato ad una codificazione dei principii di regime, vale a dire la
codificazione di quella che Mortati riteneva essere la costituzione materiale
dello Stato fascista
REMODEL. WP5. Cable Manipulation Planning, Execution and Interactive Perception. T5_5. Interactive perception. Interactive Labeling of Deformable Linear Objects. v0
No full textThe dataset contains the source code, model weights and a set of input points, camera poses and images utilized for the experimental validation on the labeling of deformable linear objects, associated to a novel algorithm called DLO-WSL. The proposed approach uses deep learning techniques aiming at the precise creation of instance masks of deformable linear objects starting from the input points provided by a user. The source code comprises a deep convolutional neural network employed for computing the correction offset to be applied at the input points. The dataset is associated with the related publication:
A. Caporali, M. Pantano, L. Janisch, D. Regulin, G. Palli and D. Lee, "A Weakly Supervised Semi-Automatic Image Labeling Approach for Deformable Linear Objects," in IEEE Robotics and Automation Letters, vol. 8, no. 2, pp. 1013-1020, Feb. 2023, doi: 10.1109/LRA.2023.3234799
REMODEL. WP4. Vision-based Perception. T4_3. Cable Detection and Tracking. Fast Segmentation of Deformable Linear Objects. v0
No full textThe dataset contains the source code and model weights utilized for the experimental validation on segmentation of deformable linear objects. The developed approach is called FASTDLO. The source code algorithm comprises a deep convolutional neural network employed for background segmentation, the intersections between different Deformable Linear Objects (DLOs) are solved with a similarity-based network combined to a skeletonization algorithm. FASTDLO also describes each DLO instance with a sequence of 2D coordinates. The associated publication is the following:
A. Caporali, K. Galassi, R. Zanella and G. Palli, "FASTDLO: Fast Deformable Linear Objects Instance Segmentation," in IEEE Robotics and Automation Letters, vol. 7, no. 4, pp. 9075-9082, Oct. 2022, doi: 10.1109/LRA.2022.3189791
INTELLIMAN. WP5. Grasping, Manipulation and Arm-Hand Coordination. T5_1. Data Fusion and Sensing Technology. Text-based Deformable Linear Objects Perception. v0
No full textThe dataset contains the source code and model weights utilized for the experimental validation on segmentation of deformable linear objects with text prompts. The developed approach is called DLO Perceiver. The method employs the integration of language-based inputs to simplify the perception task of deformable linear objects. In particular, the input image is augmented with a text-based prompt guiding the segmentation of the target DLO. After encoding the image and text separately, a Perceiver-inspired structure is exploited to compress the concatenated data into transformer layers and generate the output mask from a latent vector representation. The data were produced in the framework of Horizon Europe IntelliMan project and were presented in the following publication:
A. Caporali, K. Galassi and G. Palli, "DLO Perceiver: Grounding Large Language Model for Deformable Linear Objects Perception", in IEEE Robotics and Automation Letters, vol. 9, no. 12, pp. 11385-11392, Dec. 2024, doi: 10.1109/LRA.2024.3491428
REMODEL. WP4. Vision-based Perception. T4_3. Cable Detection and Tracking. Reconstructing and Tracking Deformable Linear Objects. v0
No full textThe dataset contains the source code utilized for the experimental validation of the 3D deformable linear objects shape reconstruction and tracking system. The developed approach is called DLO3DS.
The developed procedure is based on a pipeline that first processes the images coming from the 2D camera extracting key topological points along the DLOs. These points are then used to model each DLO with a B-spline curve. Finally, the set of splines obtained from all the images is matched by exploiting a multi-view stereo-based algorithm. The associated publication is the following:
A. Caporali, K. Galassi and G. Palli, "Deformable Linear Objects 3D Shape Estimation and Tracking From Multiple 2D Views," in IEEE Robotics and Automation Letters, vol. 8, no. 6, pp. 3852-3859, June 2023, doi: 10.1109/LRA.2023.3273518
REMODEL. WP5. Cable Manipulation Planning Execution And Interactive Perception. T5_2. Cable Grasping. Clothes Detection and Grasping. v0
No full textThe dataset contains the source code of the vision algorithm utilized during the experiments of detection and manipulation of clothes carried out in the framework of REMODEL project. Specifically, the experiments were focused on the robotized picking of clothes form a laundry bin and their insertion in a washing machine drum, with also a recovery picking from the drum door region in case some large cloth remained partially out from the washing machine.
The results of these experiments are described in:
A. Caporali, W. B. Bedada and G. Palli, "A Cyber-Physical System for Clothes Detection, Manipulation and Washing Machine Loading," 2021 4th IEEE International Conference on Industrial Cyber-Physical Systems (ICPS), 2021, pp. 519-524, doi: 10.1109/ICPS49255.2021.9468189
REMODEL. WP4. Vision-based Perception. T4_3. Cable detection and tracking. 3D DLO Shape Detection and Grasp Planning from Multiple 2D Views. v0
No full textThe dataset contains the 3D estimation results of a method for 3D shape detection and grasp planning of deformable linear objects. In particular, the data are used to evaluate the different trajectory (linear and angular) approach for the evaluation of the DLO shape with different number of acquisitions. Other test involves the evaluation of the quality and the usability of the reconstruction by grasping the detected object. The data contains information about the starting point of the trajectories and the result using a different number of samples. More specific information about the method and the result can be found in the paper:
A. Caporali, K. Galassi and G. Palli, "3D DLO Shape Detection and Grasp Planning from Multiple 2D Views," 2021 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM), 2021, pp. 424-429, doi: 10.1109/AIM46487.2021.9517655
REMODEL. WP4. Vision-based Perception. T4_3. Cable Detection and Tracking. Segmentation of Deformable Linear Objects. v0
No full textThe dataset contains the source code and model weights utilized for the experimental validation on segmentation of deformable linear objects, associated to a novel algorithm called Ariadne+. The proposed approach uses deep learning and standard computer vision techniques aiming at their reliable and time effective instance segmentation of wires. The source code comprises a deep convolutional neural network employed for generating a binary mask showing where wires are present in the input image, and graph theory applied to create the wire paths from the binary mask through an iterative approach maximizing the graph coverage. In addition, a B-Spline model of each instance is provided. The dataset is associated to the related publication:
A. Caporali, R. Zanella, D. D. Greogrio and G. Palli, "Ariadne+: Deep Learning--Based Augmented Framework for the Instance Segmentation of Wires," in IEEE Transactions on Industrial Informatics, vol. 18, no. 12, pp. 8607-8617, Dec. 2022, doi: 10.1109/TII.2022.3154477
REMODEL. WP4. Vision-based Perception. T4_3. Cable Detection and Tracking. Real Time Segmentation of Deformable Linear Objects. v0
No full textThe dataset contains the source code and model weights utilized for the experimental validation on segmentation of deformable linear objects. The developed approach is called RT-DLO.
A novel method to separate the DLO instances is applied. It employs the generation of a graph representation of the scene given the semantic mask where the graph nodes are sampled from the DLOs center-lines whereas the graph edges are selected based on topological reasoning. RT-DLO also describes each DLO instance with a sequence of 2D coordinates. The associated publication is the following:
A. Caporali, K. Galassi, B. L. Žagar, R. Zanella, G. Palli and A. C. Knoll, "RT-DLO: Real-Time Deformable Linear Objects Instance Segmentation," in IEEE Transactions on Industrial Informatics, doi: 10.1109/TII.2023.3245641
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