A multimodal dataset for robotic peg extraction based on Bioin-Tacto sensor modulesMendeley Data
Robots need to adapt to the complexities of acting in unstructured environments. Robotic systems need more awareness of the environment to adapt to uncertainty and variability. Although cameras have been predominantly used in robotic tasks, the limitations that come with them, such as occlusion, vis...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-04-01
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| Series: | Data in Brief |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352340925000885 |
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| author | Viral Galayia Ruslan Masinjila Soheil Khatibi Thiago Eustaquio Alves de Oliveira Xianta Jiang Vinicius Prado da Fonseca |
| author_facet | Viral Galayia Ruslan Masinjila Soheil Khatibi Thiago Eustaquio Alves de Oliveira Xianta Jiang Vinicius Prado da Fonseca |
| author_sort | Viral Galayia |
| collection | DOAJ |
| description | Robots need to adapt to the complexities of acting in unstructured environments. Robotic systems need more awareness of the environment to adapt to uncertainty and variability. Although cameras have been predominantly used in robotic tasks, the limitations that come with them, such as occlusion, visibility, and lack of information, have diverted some focus to tactile sensing. Extensive datasets of the physical interactions between tactile-enabled robots are required to investigate and develop methods for performing manipulation and object exploration tasks. Therefore, this motivates us to compose a dataset of signals from Bioin-Tacto modules mounted on a robotic gripper performing extraction tasks. An operator controls a robotic gripper to extract three pegs of various complexities from their corresponding holes. This dataset contains angular velocity, linear acceleration, magnetic field intensity and direction, and pressure exerted on two tactile modules embedded in the compliant structure of the sensing module. The dataset comprises 96 extraction episodes, including data collected from a reinforcement learning agent. The dataset can be used to pre-train a reinforcement machine learning model to perform peg-in-hole tasks and to study how pretraining affects a manipulator's ability to infer tactile signals and improve the success rates of the manipulator. |
| format | Article |
| id | doaj-art-2ce8ef6bc9e543ed849360ed975542ef |
| institution | Kabale University |
| issn | 2352-3409 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Data in Brief |
| spelling | doaj-art-2ce8ef6bc9e543ed849360ed975542ef2025-02-08T05:00:35ZengElsevierData in Brief2352-34092025-04-0159111356A multimodal dataset for robotic peg extraction based on Bioin-Tacto sensor modulesMendeley DataViral Galayia0Ruslan Masinjila1Soheil Khatibi2Thiago Eustaquio Alves de Oliveira3Xianta Jiang4Vinicius Prado da Fonseca5Department of Computer Science, Memorial University of Newfoundland, St. John's, NL, CanadaDepartment of Computer Science, Memorial University of Newfoundland, St. John's, NL, CanadaDepartment of Computer Science, Lakehead University, Orillia, ON, CanadaDepartment of Computer Science, Lakehead University, Orillia, ON, CanadaDepartment of Computer Science, Memorial University of Newfoundland, St. John's, NL, CanadaDepartment of Computer Science, Memorial University of Newfoundland, St. John's, NL, Canada; Corresponding author.Robots need to adapt to the complexities of acting in unstructured environments. Robotic systems need more awareness of the environment to adapt to uncertainty and variability. Although cameras have been predominantly used in robotic tasks, the limitations that come with them, such as occlusion, visibility, and lack of information, have diverted some focus to tactile sensing. Extensive datasets of the physical interactions between tactile-enabled robots are required to investigate and develop methods for performing manipulation and object exploration tasks. Therefore, this motivates us to compose a dataset of signals from Bioin-Tacto modules mounted on a robotic gripper performing extraction tasks. An operator controls a robotic gripper to extract three pegs of various complexities from their corresponding holes. This dataset contains angular velocity, linear acceleration, magnetic field intensity and direction, and pressure exerted on two tactile modules embedded in the compliant structure of the sensing module. The dataset comprises 96 extraction episodes, including data collected from a reinforcement learning agent. The dataset can be used to pre-train a reinforcement machine learning model to perform peg-in-hole tasks and to study how pretraining affects a manipulator's ability to infer tactile signals and improve the success rates of the manipulator.http://www.sciencedirect.com/science/article/pii/S2352340925000885Peg-in-holeDynamic explorationTactile sensorReinforcement learning |
| spellingShingle | Viral Galayia Ruslan Masinjila Soheil Khatibi Thiago Eustaquio Alves de Oliveira Xianta Jiang Vinicius Prado da Fonseca A multimodal dataset for robotic peg extraction based on Bioin-Tacto sensor modulesMendeley Data Data in Brief Peg-in-hole Dynamic exploration Tactile sensor Reinforcement learning |
| title | A multimodal dataset for robotic peg extraction based on Bioin-Tacto sensor modulesMendeley Data |
| title_full | A multimodal dataset for robotic peg extraction based on Bioin-Tacto sensor modulesMendeley Data |
| title_fullStr | A multimodal dataset for robotic peg extraction based on Bioin-Tacto sensor modulesMendeley Data |
| title_full_unstemmed | A multimodal dataset for robotic peg extraction based on Bioin-Tacto sensor modulesMendeley Data |
| title_short | A multimodal dataset for robotic peg extraction based on Bioin-Tacto sensor modulesMendeley Data |
| title_sort | multimodal dataset for robotic peg extraction based on bioin tacto sensor modulesmendeley data |
| topic | Peg-in-hole Dynamic exploration Tactile sensor Reinforcement learning |
| url | http://www.sciencedirect.com/science/article/pii/S2352340925000885 |
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