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Review

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

AI for Manufacturing Laboratory, Department of Mechanical and Mechatronics Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada
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Academic Editors: Giuseppe Carbone and Alessandro Di Nuovo
Robotics 2021, 10(3), 105; https://doi.org/10.3390/robotics10030105
Received: 28 July 2021 / Revised: 30 August 2021 / Accepted: 6 September 2021 / Published: 13 September 2021
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. View Full-Text
Keywords: reinforcement learning; Markov decision process; policy optimization; robotic control; simulation environment; pose estimation; imitation learning reinforcement learning; Markov decision process; policy optimization; robotic control; simulation environment; pose estimation; imitation learning
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MDPI and ACS Style

Lobbezoo, A.; Qian, Y.; Kwon, H.-J. Reinforcement Learning for Pick and Place Operations in Robotics: A Survey. Robotics 2021, 10, 105. https://doi.org/10.3390/robotics10030105

AMA Style

Lobbezoo A, Qian Y, Kwon H-J. Reinforcement Learning for Pick and Place Operations in Robotics: A Survey. Robotics. 2021; 10(3):105. https://doi.org/10.3390/robotics10030105

Chicago/Turabian Style

Lobbezoo, Andrew, Yanjun Qian, and Hyock-Ju Kwon. 2021. "Reinforcement Learning for Pick and Place Operations in Robotics: A Survey" Robotics 10, no. 3: 105. https://doi.org/10.3390/robotics10030105

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