MoMa-Pos: An Efficient Object-Kinematic-Aware Base Placement Optimization Framework for Mobile Manipulation
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866916458530865152 |
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| author | Shao, Beichen Cao, Nieqing Ding, Yan Wang, Xingchen Gu, Fuqiang Chen, Chao |
| author_facet | Shao, Beichen Cao, Nieqing Ding, Yan Wang, Xingchen Gu, Fuqiang Chen, Chao |
| contents | In this work, we present MoMa-Pos, a framework that optimizes base placement for mobile manipulators, focusing on navigation-manipulation tasks in environments with both rigid and articulated objects. Base placement is particularly critical in such environments, where improper positioning can severely hinder task execution if the object's kinematics are not adequately accounted for. MoMa-Pos selectively reconstructs the environment by prioritizing task-relevant key objects, enhancing computational efficiency and ensuring that only essential kinematic details are processed. The framework leverages a graph-based neural network to predict object importance, allowing for focused modeling while minimizing unnecessary computations. Additionally, MoMa-Pos integrates inverse reachability maps with environmental kinematic properties to identify feasible base positions tailored to the specific robot model. Extensive evaluations demonstrate that MoMa-Pos outperforms existing methods in both real and simulated environments, offering improved efficiency, precision, and adaptability across diverse settings and robot models. Supplementary material can be found at https://yding25.com/MoMa-Pos |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_19940 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | MoMa-Pos: An Efficient Object-Kinematic-Aware Base Placement Optimization Framework for Mobile Manipulation Shao, Beichen Cao, Nieqing Ding, Yan Wang, Xingchen Gu, Fuqiang Chen, Chao Robotics In this work, we present MoMa-Pos, a framework that optimizes base placement for mobile manipulators, focusing on navigation-manipulation tasks in environments with both rigid and articulated objects. Base placement is particularly critical in such environments, where improper positioning can severely hinder task execution if the object's kinematics are not adequately accounted for. MoMa-Pos selectively reconstructs the environment by prioritizing task-relevant key objects, enhancing computational efficiency and ensuring that only essential kinematic details are processed. The framework leverages a graph-based neural network to predict object importance, allowing for focused modeling while minimizing unnecessary computations. Additionally, MoMa-Pos integrates inverse reachability maps with environmental kinematic properties to identify feasible base positions tailored to the specific robot model. Extensive evaluations demonstrate that MoMa-Pos outperforms existing methods in both real and simulated environments, offering improved efficiency, precision, and adaptability across diverse settings and robot models. Supplementary material can be found at https://yding25.com/MoMa-Pos |
| title | MoMa-Pos: An Efficient Object-Kinematic-Aware Base Placement Optimization Framework for Mobile Manipulation |
| topic | Robotics |
| url | https://arxiv.org/abs/2403.19940 |