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

Mohammed, Marwan Qaid; Kwek, Lee Chung; Chyi, Chua Shing

doi:10.1109/access.2020.3027923

Cited by 73 publications

(51 citation statements)

References 240 publications

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“…Caldera et al ( 2018 ), Kroemer et al ( 2019 ), Li and Qiao ( 2019 ), and Kleeberger et al ( 2020 ) focus on the overview of robot manipulation methods based on deep learning. Mohammed et al ( 2020 ) and Zhao W. et al ( 2020 ) introduce the techniques in robot learning on the basis of reinforcement learning. Billard and Kragic ( 2019 ) describes the trends and challenges in robot manipulation.…”

Section: Proposed Taxonomymentioning

confidence: 99%

Robotics Dexterous Grasping: The Methods Based on Point Cloud and Deep Learning

et al. 2021

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Dexterous manipulation, especially dexterous grasping, is a primitive and crucial ability of robots that allows the implementation of performing human-like behaviors. Deploying the ability on robots enables them to assist and substitute human to accomplish more complex tasks in daily life and industrial production. A comprehensive review of the methods based on point cloud and deep learning for robotics dexterous grasping from three perspectives is given in this paper. As a new category schemes of the mainstream methods, the proposed generation-evaluation framework is the core concept of the classification. The other two classifications based on learning modes and applications are also briefly described afterwards. This review aims to afford a guideline for robotics dexterous grasping researchers and developers.

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Section: Proposed Taxonomymentioning

confidence: 99%

Robotics Dexterous Grasping: The Methods Based on Point Cloud and Deep Learning

et al. 2021

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“…A broad-scale generalized strategy for implementing pick-and-place with RL in robotics is needed before this technique can be implemented at scale. Mohammed and Chua [12], Liu et al [13], and Tai et al [14] have all written review papers focused on RL in robotics; however, these papers have a broad range of focus in terms of robotic agents used and the task completed.…”

Section: Related Workmentioning

confidence: 99%

“…Many other papers exist, which can be applicable for subtasks of pick-and-place, however most of them could not be easily extended to the entire operation. For more samples of individual grasping techniques for the pick action, this author recommends the review of [12].…”

Section: State Of Research-pick-and-place Subtasksmentioning

confidence: 99%

Reinforcement Learning for Pick and Place Operations in Robotics: A Survey

2021

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The field of robotics has been rapidly developing in recent years, and the work related to training robotic agents with reinforcement learning has been a major focus of research. This survey reviews the application of reinforcement learning for pick-and-place operations, a task that a logistics robot can be trained to complete without support from a robotics engineer. To introduce this topic, we first review the fundamentals of reinforcement learning and various methods of policy optimization, such as value iteration and policy search. Next, factors which have an impact on the pick-and-place task, such as reward shaping, imitation learning, pose estimation, and simulation environment are examined. Following the review of the fundamentals and key factors for reinforcement learning, we present an extensive review of all methods implemented by researchers in the field to date. The strengths and weaknesses of each method from literature are discussed, and details about the contribution of each manuscript to the field are reviewed. The concluding critical discussion of the available literature, and the summary of open problems indicates that experiment validation, model generalization, and grasp pose selection are topics that require additional research.

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“…Recently, ref. [8] has reviewed current and future works in deep reinforcement learning (DRL)-based grasping in clutter. A good review of recent and future works on a cognitive enabled robot for performing reaching and grasping tasks can be found in ref.…”

Section: Introductionmentioning

confidence: 99%

Comprehensive Review on Reaching and Grasping of Objects in Robotics

Mohammed¹,

Chua²,

Kwek³

2021

Robotica

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SUMMARY Interaction between a robot and its environment requires perception about the environment, which helps the robot in making a clear decision about the object type and its location. After that, the end effector will be brought to the object’s location for grasping. There are many research studies on the reaching and grasping of objects using different techniques and mechanisms for increasing accuracy and robustness during grasping and reaching tasks. Thus, this paper presents an extensive review of research directions and topics of different approaches such as sensing, learning and gripping, which have been implemented within the current five years.

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

Cited by 73 publications

References 240 publications

Robotics Dexterous Grasping: The Methods Based on Point Cloud and Deep Learning

Robotics Dexterous Grasping: The Methods Based on Point Cloud and Deep Learning

Reinforcement Learning for Pick and Place Operations in Robotics: A Survey

Comprehensive Review on Reaching and Grasping of Objects in Robotics

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