CATCH-FORM-ACTer: Compliance-Aware Tactile Control and Hybrid Deformation Regulation-Based Action Transformer for Viscoelastic Object Manipulation
Automating contact-rich manipulation of viscoelastic objects with rigid robots faces challenges including dynamic parameter mismatches, unstable contact oscillations, and spatiotemporal force-deformation coupling. In our prior work, a Compliance-Aware Tactile Control and Hybrid Deformation Regulatio...
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2025-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/11088097/ |
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| author | Haobo Kang Hongjun Ma Weichang Li |
| author_facet | Haobo Kang Hongjun Ma Weichang Li |
| author_sort | Haobo Kang |
| collection | DOAJ |
| description | Automating contact-rich manipulation of viscoelastic objects with rigid robots faces challenges including dynamic parameter mismatches, unstable contact oscillations, and spatiotemporal force-deformation coupling. In our prior work, a Compliance-Aware Tactile Control and Hybrid Deformation Regulation (CATCH-FORM-3D) strategy fulfills robust and effective manipulations of 3D viscoelastic objects, which combines a contact force-driven admittance outer loop and a PDE-stabilized inner loop, achieving sub-millimeter surface deformation accuracy and ±5% force tracking. However, this strategy requires fine-tuning of object-specific parameters and task-specific calibrations, to bridge this gap, a CATCH-FORM-ACTer is proposed, by enhancing CATCH-FORM-3D with a framework of Action Chunking with Transformer (ACT). An intuitive teleoperation system performs Learning from Demonstration (LfD) to build up a long-horizon sensing, decision-making and execution sequences. Unlike conventional ACT methods focused solely on trajectory planning, our approach dynamically adjusts stiffness, damping, and diffusion parameters in real time during multi-phase manipulations, effectively imitating human-like force-deformation modulation. Experiments on single arm/bimanual robots in three tasks show better force fields patterns and thus <inline-formula> <tex-math notation="LaTeX">$10\%-20\%$ </tex-math></inline-formula> higher success rates versus conventional methods, enabling precise, safe interactions for industrial, medical or household scenarios. |
| format | Article |
| id | doaj-art-dcc0922637f34c5482d6418a7fc10e29 |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
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| series | IEEE Access |
| spelling | doaj-art-dcc0922637f34c5482d6418a7fc10e292025-08-20T03:16:12ZengIEEEIEEE Access2169-35362025-01-011313199813200510.1109/ACCESS.2025.359149911088097CATCH-FORM-ACTer: Compliance-Aware Tactile Control and Hybrid Deformation Regulation-Based Action Transformer for Viscoelastic Object ManipulationHaobo Kang0Hongjun Ma1https://orcid.org/0000-0001-5739-8011Weichang Li2School of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou, ChinaSchool of Automation Science and Engineering, South China University of Technology, Guangzhou, ChinaSchool of Automation Science and Engineering, South China University of Technology, Guangzhou, ChinaAutomating contact-rich manipulation of viscoelastic objects with rigid robots faces challenges including dynamic parameter mismatches, unstable contact oscillations, and spatiotemporal force-deformation coupling. In our prior work, a Compliance-Aware Tactile Control and Hybrid Deformation Regulation (CATCH-FORM-3D) strategy fulfills robust and effective manipulations of 3D viscoelastic objects, which combines a contact force-driven admittance outer loop and a PDE-stabilized inner loop, achieving sub-millimeter surface deformation accuracy and ±5% force tracking. However, this strategy requires fine-tuning of object-specific parameters and task-specific calibrations, to bridge this gap, a CATCH-FORM-ACTer is proposed, by enhancing CATCH-FORM-3D with a framework of Action Chunking with Transformer (ACT). An intuitive teleoperation system performs Learning from Demonstration (LfD) to build up a long-horizon sensing, decision-making and execution sequences. Unlike conventional ACT methods focused solely on trajectory planning, our approach dynamically adjusts stiffness, damping, and diffusion parameters in real time during multi-phase manipulations, effectively imitating human-like force-deformation modulation. Experiments on single arm/bimanual robots in three tasks show better force fields patterns and thus <inline-formula> <tex-math notation="LaTeX">$10\%-20\%$ </tex-math></inline-formula> higher success rates versus conventional methods, enabling precise, safe interactions for industrial, medical or household scenarios.https://ieeexplore.ieee.org/document/11088097/Contact-rich manipulationvision-tactile perceptionviscoelastic materialslearning from demonstrationrobotic hand3D Kelvin–Voigt and Maxwell models |
| spellingShingle | Haobo Kang Hongjun Ma Weichang Li CATCH-FORM-ACTer: Compliance-Aware Tactile Control and Hybrid Deformation Regulation-Based Action Transformer for Viscoelastic Object Manipulation IEEE Access Contact-rich manipulation vision-tactile perception viscoelastic materials learning from demonstration robotic hand 3D Kelvin–Voigt and Maxwell models |
| title | CATCH-FORM-ACTer: Compliance-Aware Tactile Control and Hybrid Deformation Regulation-Based Action Transformer for Viscoelastic Object Manipulation |
| title_full | CATCH-FORM-ACTer: Compliance-Aware Tactile Control and Hybrid Deformation Regulation-Based Action Transformer for Viscoelastic Object Manipulation |
| title_fullStr | CATCH-FORM-ACTer: Compliance-Aware Tactile Control and Hybrid Deformation Regulation-Based Action Transformer for Viscoelastic Object Manipulation |
| title_full_unstemmed | CATCH-FORM-ACTer: Compliance-Aware Tactile Control and Hybrid Deformation Regulation-Based Action Transformer for Viscoelastic Object Manipulation |
| title_short | CATCH-FORM-ACTer: Compliance-Aware Tactile Control and Hybrid Deformation Regulation-Based Action Transformer for Viscoelastic Object Manipulation |
| title_sort | catch form acter compliance aware tactile control and hybrid deformation regulation based action transformer for viscoelastic object manipulation |
| topic | Contact-rich manipulation vision-tactile perception viscoelastic materials learning from demonstration robotic hand 3D Kelvin–Voigt and Maxwell models |
| url | https://ieeexplore.ieee.org/document/11088097/ |
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