GAMMA: Graspability-Aware Mobile MAnipulation Policy Learning based on Online Grasping Pose Fusion
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866911787566235648 |
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| author | Zhang, Jiazhao Gireesh, Nandiraju Wang, Jilong Fang, Xiaomeng Xu, Chaoyi Chen, Weiguang Dai, Liu Wang, He |
| author_facet | Zhang, Jiazhao Gireesh, Nandiraju Wang, Jilong Fang, Xiaomeng Xu, Chaoyi Chen, Weiguang Dai, Liu Wang, He |
| contents | Mobile manipulation constitutes a fundamental task for robotic assistants and garners significant attention within the robotics community. A critical challenge inherent in mobile manipulation is the effective observation of the target while approaching it for grasping. In this work, we propose a graspability-aware mobile manipulation approach powered by an online grasping pose fusion framework that enables a temporally consistent grasping observation. Specifically, the predicted grasping poses are online organized to eliminate the redundant, outlier grasping poses, which can be encoded as a grasping pose observation state for reinforcement learning. Moreover, on-the-fly fusing the grasping poses enables a direct assessment of graspability, encompassing both the quantity and quality of grasping poses. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2309_15459 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | GAMMA: Graspability-Aware Mobile MAnipulation Policy Learning based on Online Grasping Pose Fusion Zhang, Jiazhao Gireesh, Nandiraju Wang, Jilong Fang, Xiaomeng Xu, Chaoyi Chen, Weiguang Dai, Liu Wang, He Robotics Computer Vision and Pattern Recognition Mobile manipulation constitutes a fundamental task for robotic assistants and garners significant attention within the robotics community. A critical challenge inherent in mobile manipulation is the effective observation of the target while approaching it for grasping. In this work, we propose a graspability-aware mobile manipulation approach powered by an online grasping pose fusion framework that enables a temporally consistent grasping observation. Specifically, the predicted grasping poses are online organized to eliminate the redundant, outlier grasping poses, which can be encoded as a grasping pose observation state for reinforcement learning. Moreover, on-the-fly fusing the grasping poses enables a direct assessment of graspability, encompassing both the quantity and quality of grasping poses. |
| title | GAMMA: Graspability-Aware Mobile MAnipulation Policy Learning based on Online Grasping Pose Fusion |
| topic | Robotics Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2309.15459 |