Self-Supervised Learning for Joint Pushing and Grasping Policies in Highly Cluttered Environments
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2022
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| _version_ | 1866910368682475520 |
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| author | Wang, Yongliang Mokhtar, Kamal Heemskerk, Cock Kasaei, Hamidreza |
| author_facet | Wang, Yongliang Mokhtar, Kamal Heemskerk, Cock Kasaei, Hamidreza |
| contents | Robots often face situations where grasping a goal object is desirable but not feasible due to other present objects preventing the grasp action. We present a deep Reinforcement Learning approach to learn grasping and pushing policies for manipulating a goal object in highly cluttered environments to address this problem. In particular, a dual Reinforcement Learning model approach is proposed, which presents high resilience in handling complicated scenes, reaching an average of 98% task completion using primitive objects in a simulation environment. To evaluate the performance of the proposed approach, we performed two extensive sets of experiments in packed objects and a pile of object scenarios with a total of 1000 test runs in simulation. Experimental results showed that the proposed method worked very well in both scenarios and outperformed the recent state-of-the-art approaches. Demo video, trained models, and source code for the results reproducibility purpose are publicly available. https://sites.google.com/view/pushandgrasp/home |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2203_02511 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Self-Supervised Learning for Joint Pushing and Grasping Policies in Highly Cluttered Environments Wang, Yongliang Mokhtar, Kamal Heemskerk, Cock Kasaei, Hamidreza Robotics Robots often face situations where grasping a goal object is desirable but not feasible due to other present objects preventing the grasp action. We present a deep Reinforcement Learning approach to learn grasping and pushing policies for manipulating a goal object in highly cluttered environments to address this problem. In particular, a dual Reinforcement Learning model approach is proposed, which presents high resilience in handling complicated scenes, reaching an average of 98% task completion using primitive objects in a simulation environment. To evaluate the performance of the proposed approach, we performed two extensive sets of experiments in packed objects and a pile of object scenarios with a total of 1000 test runs in simulation. Experimental results showed that the proposed method worked very well in both scenarios and outperformed the recent state-of-the-art approaches. Demo video, trained models, and source code for the results reproducibility purpose are publicly available. https://sites.google.com/view/pushandgrasp/home |
| title | Self-Supervised Learning for Joint Pushing and Grasping Policies in Highly Cluttered Environments |
| topic | Robotics |
| url | https://arxiv.org/abs/2203.02511 |