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

Mokhtar, Kamal (2022) Self-Supervised Learning for Joint Pushing and Grasping Policies in Highly Cluttered Environments. Master's Thesis / Essay, Artificial Intelligence.


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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. We also conduct qualitative testing using the service robot TIAGo in real-life. To evaluate the performance of the proposed approach, we performed two extensive sets of simulation 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

Item Type: Thesis (Master's Thesis / Essay)
Supervisor name: Mohades Kasaei, S.H.
Degree programme: Artificial Intelligence
Thesis type: Master's Thesis / Essay
Language: English
Date Deposited: 02 Aug 2022 07:49
Last Modified: 02 Aug 2022 07:49

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