Probabilistic Task Parameterization of Tool-Tissue Interaction via Sparse Landmarks Tracking in Robotic Surgery
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866910912046170112 |
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| author | Wang, Yiting Fan, Yunxin Liu, Fei |
| author_facet | Wang, Yiting Fan, Yunxin Liu, Fei |
| contents | Accurate modeling of tool-tissue interactions in robotic surgery requires precise tracking of deformable tissues and integration of surgical domain knowledge. Traditional methods rely on labor-intensive annotations or rigid assumptions, limiting flexibility. We propose a framework combining sparse keypoint tracking and probabilistic modeling that propagates expert-annotated landmarks across endoscopic frames, even with large tissue deformations. Clustered tissue keypoints enable dynamic local transformation construction via PCA, and tool poses, tracked similarly, are expressed relative to these frames. Embedding these into a Task-Parameterized Gaussian Mixture Model (TP-GMM) integrates data-driven observations with labeled clinical expertise, effectively predicting relative tool-tissue poses and enhancing visual understanding of robotic surgical motions directly from video data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_11495 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Probabilistic Task Parameterization of Tool-Tissue Interaction via Sparse Landmarks Tracking in Robotic Surgery Wang, Yiting Fan, Yunxin Liu, Fei Robotics Computer Vision and Pattern Recognition Machine Learning Systems and Control Accurate modeling of tool-tissue interactions in robotic surgery requires precise tracking of deformable tissues and integration of surgical domain knowledge. Traditional methods rely on labor-intensive annotations or rigid assumptions, limiting flexibility. We propose a framework combining sparse keypoint tracking and probabilistic modeling that propagates expert-annotated landmarks across endoscopic frames, even with large tissue deformations. Clustered tissue keypoints enable dynamic local transformation construction via PCA, and tool poses, tracked similarly, are expressed relative to these frames. Embedding these into a Task-Parameterized Gaussian Mixture Model (TP-GMM) integrates data-driven observations with labeled clinical expertise, effectively predicting relative tool-tissue poses and enhancing visual understanding of robotic surgical motions directly from video data. |
| title | Probabilistic Task Parameterization of Tool-Tissue Interaction via Sparse Landmarks Tracking in Robotic Surgery |
| topic | Robotics Computer Vision and Pattern Recognition Machine Learning Systems and Control |
| url | https://arxiv.org/abs/2504.11495 |