An experience-based policy gradient method for smooth manipulation

Wang, Yongchao; Lan, Xuguang; Feng, Chao; Wan, Lipeng; Li, Jin; Liu, Yuwang; Li, Decai

doi:10.1109/cyber46603.2019.9066580

Cited by 3 publications

(2 citation statements)

References 6 publications

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“…Wang et al also proposed the experience-based policy gradient method (EBDDPG), which promotes smooth robot movements. Results demonstrated that this method improves the success rate of grasping tasks and encourages smoother manipulation [156]. Controlling the gripping of a robot arm can be improved by using the enhanced DDPG reinforcement learning algorithm introduced by Qi and Li [157].…”

Section: Deep Deterministic Policy Gradientmentioning

confidence: 98%

Review of Reinforcement Learning for Robotic Grasping: Analysis and Recommendations

Sekkat,

Moutik,

Ourabah

et al. 2023

Stat., optim. inf. comput.

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This review paper provides a comprehensive analysis of over 100 research papers focused on the challenges of robotic grasping and the effectiveness of various machine learning techniques, particularly those utilizing Deep Neural Networks (DNNs) and Reinforcement Learning (RL). The objective of this review is to simplify the research process for others by gathering different forms of Deep Reinforcement Learning (DRL) grasping tasks in one place. Through a thorough analysis of the literature, the study emphasizes the critical nature of grasping for robots and how DRL techniques, particularly the Soft-Actor-Critic (SAC) strategy, have demonstrated high efficiency in handling the task. The results of this study hold significant implications for the development of more advanced and efficient grasping systems for robots. Continued research in this area is crucial to further enhance the capabilities of robots in handling complex and challenging tasks, such as grasping.

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Section: Deep Deterministic Policy Gradientmentioning

confidence: 98%

Review of Reinforcement Learning for Robotic Grasping: Analysis and Recommendations

Sekkat,

Moutik,

Ourabah

et al. 2023

Stat., optim. inf. comput.

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“…The grasping performance was evaluated on three different objects: cube, sphere and cylinder, and the highest accuracy (91.1%) was achieved by the use of a multi-view camera setup on cube object. In a very similar scenario [74], not only the grasping problem was addressed but also the smoothness of the trajectory by proposing a policy-based DRL method: experience-based deep deterministic policy gradient. The grasping accuracy of 90.68% was achieved.…”

Section: Typical Grasping Scenariosmentioning

confidence: 99%

Smart Industrial Robot Control Trends, Challenges and Opportunities within Manufacturing

2022

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Industrial robots and associated control methods are continuously developing. With the recent progress in the field of artificial intelligence, new perspectives in industrial robot control strategies have emerged, and prospects towards cognitive robots have arisen. AI-based robotic systems are strongly becoming one of the main areas of focus, as flexibility and deep understanding of complex manufacturing processes are becoming the key advantage to raise competitiveness. This review first expresses the significance of smart industrial robot control in manufacturing towards future factories by listing the needs, requirements and introducing the envisioned concept of smart industrial robots. Secondly, the current trends that are based on different learning strategies and methods are explored. Current computer-vision, deep reinforcement learning and imitation learning based robot control approaches and possible applications in manufacturing are investigated. Gaps, challenges, limitations and open issues are identified along the way.

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