EqvAfford: SE(3) Equivariance for Point-Level Affordance Learning
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866911980189646848 |
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| author | Chen, Yue Tie, Chenrui Wu, Ruihai Dong, Hao |
| author_facet | Chen, Yue Tie, Chenrui Wu, Ruihai Dong, Hao |
| contents | Humans perceive and interact with the world with the awareness of equivariance, facilitating us in manipulating different objects in diverse poses. For robotic manipulation, such equivariance also exists in many scenarios. For example, no matter what the pose of a drawer is (translation, rotation and tilt), the manipulation strategy is consistent (grasp the handle and pull in a line). While traditional models usually do not have the awareness of equivariance for robotic manipulation, which might result in more data for training and poor performance in novel object poses, we propose our EqvAfford framework, with novel designs to guarantee the equivariance in point-level affordance learning for downstream robotic manipulation, with great performance and generalization ability on representative tasks on objects in diverse poses. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2408_01953 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | EqvAfford: SE(3) Equivariance for Point-Level Affordance Learning Chen, Yue Tie, Chenrui Wu, Ruihai Dong, Hao Robotics Computer Vision and Pattern Recognition Machine Learning Humans perceive and interact with the world with the awareness of equivariance, facilitating us in manipulating different objects in diverse poses. For robotic manipulation, such equivariance also exists in many scenarios. For example, no matter what the pose of a drawer is (translation, rotation and tilt), the manipulation strategy is consistent (grasp the handle and pull in a line). While traditional models usually do not have the awareness of equivariance for robotic manipulation, which might result in more data for training and poor performance in novel object poses, we propose our EqvAfford framework, with novel designs to guarantee the equivariance in point-level affordance learning for downstream robotic manipulation, with great performance and generalization ability on representative tasks on objects in diverse poses. |
| title | EqvAfford: SE(3) Equivariance for Point-Level Affordance Learning |
| topic | Robotics Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2408.01953 |