Review of Learning-Based Robotic Manipulation in Cluttered Environments

Mohammed, Marwan Qaid; Kwek, Lee Chung; Chua, Shing Chyi; Al-Dhaqm, Arafat; Nahavandi, Saeid; Eisa, Taiseer Abdalla Elfadil; Miskon, Muhammad Fahmi; Al-Mhiqani, Mohammed Nasser; Ali, Abdulalem; Abaker, Mohammed; Alandoli, Esmail Ali

doi:10.3390/s22207938

Cited by 21 publications

(11 citation statements)

References 132 publications

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“…Therefore, the challenges are divided into two parts based on each application. Robotic grasping and manipulation have many key challenges, including dexterity and control, sample efficiency, sparse rewards, and sim-to-real transfer policies [ 12 , 13 , 90 , 91 , 92 ].…”

Section: Challenges Conclusion and Future Directionsmentioning

confidence: 99%

Reinforcement Learning Algorithms and Applications in Healthcare and Robotics: A Comprehensive and Systematic Review

Al-Hamadani,

Fadhel,

Alzubaidi

et al. 2024

Sensors

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Reinforcement learning (RL) has emerged as a dynamic and transformative paradigm in artificial intelligence, offering the promise of intelligent decision-making in complex and dynamic environments. This unique feature enables RL to address sequential decision-making problems with simultaneous sampling, evaluation, and feedback. As a result, RL techniques have become suitable candidates for developing powerful solutions in various domains. In this study, we present a comprehensive and systematic review of RL algorithms and applications. This review commences with an exploration of the foundations of RL and proceeds to examine each algorithm in detail, concluding with a comparative analysis of RL algorithms based on several criteria. This review then extends to two key applications of RL: robotics and healthcare. In robotics manipulation, RL enhances precision and adaptability in tasks such as object grasping and autonomous learning. In healthcare, this review turns its focus to the realm of cell growth problems, clarifying how RL has provided a data-driven approach for optimizing the growth of cell cultures and the development of therapeutic solutions. This review offers a comprehensive overview, shedding light on the evolving landscape of RL and its potential in two diverse yet interconnected fields.

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Section: Challenges Conclusion and Future Directionsmentioning

confidence: 99%

Reinforcement Learning Algorithms and Applications in Healthcare and Robotics: A Comprehensive and Systematic Review

Al-Hamadani,

Fadhel,

Alzubaidi

et al. 2024

Sensors

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“…Robot manipulation encompasses a wide range of tasks, from assembly operations, such as screwing and peg-in-hole, to robot grasping and pick-and-place operations [151,152]. The characteristics of DRL make it very suitable for robotic tasks, which has produced a close relationship between both fields for many years, leading to promising results in the future [50,120].…”

Section: Roboticsmentioning

confidence: 99%

A Review of Deep Reinforcement Learning Approaches for Smart Manufacturing in Industry 4.0 and 5.0 Framework

et al. 2022

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In this review, the industry’s current issues regarding intelligent manufacture are presented. This work presents the status and the potential for the I4.0 and I5.0’s revolutionary technologies. AI and, in particular, the DRL algorithms, which are a perfect response to the unpredictability and volatility of modern demand, are studied in detail. Through the introduction of RL concepts and the development of those with ANNs towards DRL, the potential and variety of these kinds of algorithms are highlighted. Moreover, because these algorithms are data based, their modification to meet the requirements of industry operations is also included. In addition, this review covers the inclusion of new concepts, such as digital twins, in response to an absent environment model and how it can improve the performance and application of DRL algorithms even more. This work highlights that DRL applicability is demonstrated across all manufacturing industry operations, outperforming conventional methodologies and, most notably, enhancing the manufacturing process’s resilience and adaptability. It is stated that there is still considerable work to be carried out in both academia and industry to fully leverage the promise of these disruptive tools, begin their deployment in industry, and take a step closer to the I5.0 industrial revolution.

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“…Analytical models for pushing require complete knowledge of the environment, including physical and geometric properties such as object pose, shape, friction parameters, and mass. Developing analytical models for unstructured environments characterized by high degrees of freedom, non-linearity, and stochasticity, such as the case of pushing a flexible stem to reach a strawberry, can be a challenging task [8].…”

Section: Introductionmentioning

confidence: 99%

Deep Functional Predictive Control for Strawberry Cluster Manipulation using Tactile Prediction

Nazari¹,

Gandolfi²,

Talebpour³

et al. 2023

Preprint

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This paper introduces a novel approach to address the problem of Physical Robot Interaction (PRI) during robot pushing tasks. The approach uses a data-driven forward model based on tactile predictions to inform the controller about potential future movements of the object being pushed, such as a strawberry stem, using a robot tactile finger. The model is integrated into a Deep Functional Predictive Control (d-FPC) system to control the displacement of the stem on the tactile finger during pushes. Pushing an object with a robot finger along a desired trajectory in 3D is a highly nonlinear and complex physical robot interaction, especially when the object is not stably grasped. The proposed approach controls the stem movements on the tactile finger in a prediction horizon. The effectiveness of the proposed FPC is demonstrated in a series of tests involving a real robot pushing a strawberry in a cluster. The results indicate that the d-FPC controller can successfully control PRI in robotic manipulation tasks beyond the handling of strawberries. The proposed approach offers a promising direction for addressing the challenging PRI problem in robotic manipulation tasks. Future work will explore the generalisation of the approach to other objects and tasks.

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Review of Learning-Based Robotic Manipulation in Cluttered Environments

Cited by 21 publications

References 132 publications

Reinforcement Learning Algorithms and Applications in Healthcare and Robotics: A Comprehensive and Systematic Review

Reinforcement Learning Algorithms and Applications in Healthcare and Robotics: A Comprehensive and Systematic Review

A Review of Deep Reinforcement Learning Approaches for Smart Manufacturing in Industry 4.0 and 5.0 Framework

Deep Functional Predictive Control for Strawberry Cluster Manipulation using Tactile Prediction

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