End-to-end nonprehensile rearrangement with deep reinforcement learning and simulation-to-reality transfer

Yuan, Weihao; Hang, Kaiyu; Kragić, Danica; Wang, Michael Y.; Stork, Johannes A.

doi:10.1016/j.robot.2019.06.007

Cited by 40 publications

(24 citation statements)

References 25 publications

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“…Notably, the system has to forgo access to raw sensor data to avoid the gap between simulation and reality. The authors addressed this issue in their next work [163]. They trained policies end to end by using the deep Q-learning algorithm with CNN, which maps raw pixels as a state-action value then transfers the policy to a real robotic application with supervised examples.…”

Section: Simulation-to-real-world Transfermentioning

confidence: 99%

Review of Deep Reinforcement Learning-Based Object Grasping: Techniques, Open Challenges, and Recommendations

2020

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The motivation behind our work is to review and analyze the most relevant studies on deep reinforcement learning-based object manipulation. Various studies are examined through a survey of existing literature and investigation of various aspects, namely, the intended applications, techniques applied, challenges faced by researchers and recommendations for minimizing obstacles. This review refers to all relevant articles on deep reinforcement learning-based object manipulation and solutions. The object grasping issue is a major manipulation challenge. Object grasping requires detection systems, methods and tools to facilitate efficient and fast agent training. Several studies have proposed that object grasping and its subtypes are the main elements in dealing with the environment and agent. Unlike other review articles, this review article provides different observations on deep reinforcement learning-based manipulation. The results of this comprehensive review of deep reinforcement learning in the manipulation field may be valuable for researchers and practitioners because they can expedite the establishment of important guidelines.

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Section: Simulation-to-real-world Transfermentioning

confidence: 99%

Review of Deep Reinforcement Learning-Based Object Grasping: Techniques, Open Challenges, and Recommendations

2020

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“…Reinforcement learning was not the focus in the early stage, but with Google's successful application in Atari and Go games, this branch of machine learning has attracted much attention. With the development of deep reinforcement learning, researchers have combined it with machine vision [138][139][140][141][142] in the hope of removing the need for labeled data and artificial means to achieve intelligence.…”

Section: Different Machine Vision Algorithms Without Labeled Datamentioning

confidence: 99%

Object Detection Recognition and Robot Grasping Based on Machine Learning: A Survey

Bai

Yang

et al. 2020

IEEE Access

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With the rapid development of machine learning, its powerful function in the machine vision field is increasingly reflected. The combination of machine vision and robotics to achieve the same precise and fast grasping as that of humans requires high-precision target detection and recognition, location and reasonable grasp strategy generation, which is the ultimate goal of global researchers and one of the prerequisites for the large-scale application of robots. Traditional machine learning has a long history and good achievements in the field of image processing and robot control. The CNN (convolutional neural network) algorithm realizes training of large-scale image datasets, solves the disadvantages of traditional machine learning in large datasets, and greatly improves accuracy, thereby positioning CNNs as a global research hotspot. However, the increasing difficulty of labeled data acquisition limits their development. Therefore, unsupervised learning, self-supervised learning and reinforcement learning, which are less dependent on labeled data, have also undergone rapid development and achieved good performance in the fields of image processing and robot capture. According to the inherent defects of vision, this paper summarizes the research achievements of tactile feedback in the fields of target recognition and robot grasping and finds that the combination of vision and tactile feedback can improve the success rate and robustness of robot grasping. This paper provides a systematic summary and analysis of the research status of machine vision and tactile feedback in the field of robot grasping and establishes a reasonable reference for future research.

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“…The autonomous solutions to the reaching through clutter problem can be categorized into three groups: There are sampling-based planning approaches [5], [6], [9], trajectory optimization based approaches [3], [14], and learning-based approaches [4], [7], [15], [16]. While these approaches show varying degrees of success, the difficult instances of this problem are still challenging for autonomous systems, due to the problem being high-dimensional and under-actuated, and also due to real-world physics uncertainty.…”

Section: Related Workmentioning

confidence: 99%

Online Replanning With Human-in-the-Loop for Non-Prehensile Manipulation in Clutter — A Trajectory Optimization Based Approach

Papallas

Cohn

Dogar

2020

IEEE Robot. Autom. Lett.

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End-to-end nonprehensile rearrangement with deep reinforcement learning and simulation-to-reality transfer

Cited by 40 publications

References 25 publications

Review of Deep Reinforcement Learning-Based Object Grasping: Techniques, Open Challenges, and Recommendations

Review of Deep Reinforcement Learning-Based Object Grasping: Techniques, Open Challenges, and Recommendations

Object Detection Recognition and Robot Grasping Based on Machine Learning: A Survey

Online Replanning With Human-in-the-Loop for Non-Prehensile Manipulation in Clutter — A Trajectory Optimization Based Approach

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