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arXiv · 2203.02511

Self-Supervised Learning for Joint Pushing and Grasping Policies in Highly Cluttered Environments

Yongliang Wang·Kamal Mokhtar·Cock Heemskerk·Hamidreza Kasaei
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Abstract

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

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Yongliang Wang, Kamal Mokhtar, Cock Heemskerk, Hamidreza Kasaei. 2022-03-04. Self-Supervised Learning for Joint Pushing and Grasping Policies in Highly Cluttered Environments. https://arxiv.org/abs/2203.02511

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