Hybrid-Diffusion Models: Combining Open-loop Routines with Visuomotor Diffusion Policies
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866912749459603456 |
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| author | Van Haastregt, Jonne Orthmann, Bastian Welle, Michael C. Zhang, Yuchong Kragic, Danica |
| author_facet | Van Haastregt, Jonne Orthmann, Bastian Welle, Michael C. Zhang, Yuchong Kragic, Danica |
| contents | Despite the fact that visuomotor-based policies obtained via imitation learning demonstrate good performances in complex manipulation tasks, they usually struggle to achieve the same accuracy and speed as traditional control based methods. In this work, we introduce Hybrid-Diffusion models that combine open-loop routines with visuomotor diffusion policies. We develop Teleoperation Augmentation Primitives (TAPs) that allow the operator to perform predefined routines, such as locking specific axes, moving to perching waypoints, or triggering task-specific routines seamlessly during demonstrations. Our Hybrid-Diffusion method learns to trigger such TAPs during inference. We validate the method on challenging real-world tasks: Vial Aspiration, Open-Container Liquid Transfer, and container unscrewing. All experimental videos are available on the project's website: https://hybriddiffusion.github.io/ |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_04960 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Hybrid-Diffusion Models: Combining Open-loop Routines with Visuomotor Diffusion Policies Van Haastregt, Jonne Orthmann, Bastian Welle, Michael C. Zhang, Yuchong Kragic, Danica Robotics Despite the fact that visuomotor-based policies obtained via imitation learning demonstrate good performances in complex manipulation tasks, they usually struggle to achieve the same accuracy and speed as traditional control based methods. In this work, we introduce Hybrid-Diffusion models that combine open-loop routines with visuomotor diffusion policies. We develop Teleoperation Augmentation Primitives (TAPs) that allow the operator to perform predefined routines, such as locking specific axes, moving to perching waypoints, or triggering task-specific routines seamlessly during demonstrations. Our Hybrid-Diffusion method learns to trigger such TAPs during inference. We validate the method on challenging real-world tasks: Vial Aspiration, Open-Container Liquid Transfer, and container unscrewing. All experimental videos are available on the project's website: https://hybriddiffusion.github.io/ |
| title | Hybrid-Diffusion Models: Combining Open-loop Routines with Visuomotor Diffusion Policies |
| topic | Robotics |
| url | https://arxiv.org/abs/2512.04960 |