Reinforcement learning coupled with finite element modeling for facial motion learning

Nguyen, Duc-Phong; Tho, M.C. Ho Ba; Dao, Tien Tuan

doi:10.1016/j.cmpb.2022.106904

Cited by 7 publications

(5 citation statements)

References 29 publications

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“…PSO-based decision-making employs a search space where each agent's position represents a potential solution, and movement within this space is influenced by personal bests and the swarm's global best. The convergence and stability of decision-making algorithms are crucial for the swarm's effectiveness [33]. Robustness is often enhanced by incorporating fault-tolerant mechanisms that allow the swarm to compensate for malfunctioning agents.…”

Section: 𝑃𝑃(𝑡𝑡 + 1) = (𝑃𝑃(𝑡𝑡) ⋅ 𝜌𝜌) + 𝛥𝛥𝑃𝑃mentioning

confidence: 99%

A review: Swarm Robotics: Cooperative Control in Multi-Agent Systems

Devi,

B S,

Lakhanpal

et al. 2024

E3S Web Conf.

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Swarm robotics epitomizes a frontier in cooperative control within multi-agent systems, where the emulation of biological swarms offers a paradigm shift in robotics. This paper delves into the mechanisms of decentralized decision-making and the emergent behaviors that arise from local interactions among autonomous robotic agents without the need for a central controller. It explores the synthesis of simple control rules that yield complex, adaptive, and scalable group behaviors, akin to those found in natural swarms. A critical examination of communication protocols elucidates how information-sharing among agents leads to the robust execution of collective tasks. The research further investigates the dynamics of role allocation, task partitioning, and redundancy, which are crucial for the resilience of swarm robotic systems. Through simulation and empirical analysis, the efficacy of swarm algorithms in various applications, including search and rescue, environmental monitoring, and collective construction, is demonstrated. The study's findings underscore the significance of bio-inspired algorithms and the potential of swarm robotic systems to adapt and thrive in unpredictable environments. The implications for the future of autonomous systems are profound, as swarm robotics paves the way for innovations in distributed artificial intelligence and robotic.

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Section: 𝑃𝑃(𝑡𝑡 + 1) = (𝑃𝑃(𝑡𝑡) ⋅ 𝜌𝜌) + 𝛥𝛥𝑃𝑃mentioning

confidence: 99%

A review: Swarm Robotics: Cooperative Control in Multi-Agent Systems

Devi,

B S,

Lakhanpal

et al. 2024

E3S Web Conf.

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show abstract

“…The basis of a DRL network is made up of an agent and an environment, following an action-reward type of operation. The interaction begins in the environment with the sending of its state to the agent, which takes an action consistent with the state received, according to which it is subsequently rewarded or penalized by the environment [4,44,[46][47][48]. RL is considered an autonomous learning technique that does not require labeled data but for which search and value function approximation are vital tools [4].…”

Section: Reinforcement Learning Conceptsmentioning

confidence: 99%

“…Current RL methods still present some challenges, namely the efficiency of the learning data and the ability to generalize to new scenarios [49]. Nevertheless, this group of techniques has been used with tremendous theoretical and practical achievements in diverse research topics such as robotics, gaming, biological systems, autonomous driving, computer vision, healthcare, and others [44,48,[50][51][52][53].…”

Section: Reinforcement Learning Conceptsmentioning

confidence: 99%

“…Finally, we will finalize our state-of-the-art review by referencing research that used reinforcement learning approaches, mostly in combination with deep learning methods. RL research has been developed in several topics, including robotics [113][114][115], design automation [25], energy management strategies for hybrid vehicles [43], parameter estimation in the context of biological systems [44,116,117], in facial motion learning [48,50,118], and have also been successfully applied in closed-world environments, such as games [51,54,119,120]. In the topic of image processing, some pertinent studies were found, especially using DRL [31,47,57,121].…”

Section: Research Using Reinforcement Learningmentioning

confidence: 99%

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Developments in Image Processing Using Deep Learning and Reinforcement Learning

Valente,

António,

Mora

et al. 2023

J. Imaging

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The growth in the volume of data generated, consumed, and stored, which is estimated to exceed 180 zettabytes in 2025, represents a major challenge both for organizations and for society in general. In addition to being larger, datasets are increasingly complex, bringing new theoretical and computational challenges. Alongside this evolution, data science tools have exploded in popularity over the past two decades due to their myriad of applications when dealing with complex data, their high accuracy, flexible customization, and excellent adaptability. When it comes to images, data analysis presents additional challenges because as the quality of an image increases, which is desirable, so does the volume of data to be processed. Although classic machine learning (ML) techniques are still widely used in different research fields and industries, there has been great interest from the scientific community in the development of new artificial intelligence (AI) techniques. The resurgence of neural networks has boosted remarkable advances in areas such as the understanding and processing of images. In this study, we conducted a comprehensive survey regarding advances in AI design and the optimization solutions proposed to deal with image processing challenges. Despite the good results that have been achieved, there are still many challenges to face in this field of study. In this work, we discuss the main and more recent improvements, applications, and developments when targeting image processing applications, and we propose future research directions in this field of constant and fast evolution.

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“…Additionally, merging a 3D face model reconstructed from the patient with an animation of practicing rehabilitation exercises can generate a realistic animation. This may help patients learn facial motion and practice rehabilitation exercises more effectively [ 42 ]. The objective of the present study was to apply these state-of-the-art methods to reconstruct the 3D face shape models of facial palsy patients in natural and mimic postures from one single image.…”

Section: Introductionmentioning

confidence: 99%

Fast 3D Face Reconstruction from a Single Image Using Different Deep Learning Approaches for Facial Palsy Patients

Nguyen

Dakpe

et al. 2022

Bioengineering

Self Cite

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The 3D reconstruction of an accurate face model is essential for delivering reliable feedback for clinical decision support. Medical imaging and specific depth sensors are accurate but not suitable for an easy-to-use and portable tool. The recent development of deep learning (DL) models opens new challenges for 3D shape reconstruction from a single image. However, the 3D face shape reconstruction of facial palsy patients is still a challenge, and this has not been investigated. The contribution of the present study is to apply these state-of-the-art methods to reconstruct the 3D face shape models of facial palsy patients in natural and mimic postures from one single image. Three different methods (3D Basel Morphable model and two 3D Deep Pre-trained models) were applied to the dataset of two healthy subjects and two facial palsy patients. The reconstructed outcomes were compared to the 3D shapes reconstructed using Kinect-driven and MRI-based information. As a result, the best mean error of the reconstructed face according to the Kinect-driven reconstructed shape is 1.5 ± 1.1 mm. The best error range is 1.9 ± 1.4 mm when compared to the MRI-based shapes. Before using the procedure to reconstruct the 3D faces of patients with facial palsy or other facial disorders, several ideas for increasing the accuracy of the reconstruction can be discussed based on the results. This present study opens new avenues for the fast reconstruction of the 3D face shapes of facial palsy patients from a single image. As perspectives, the best DL method will be implemented into our computer-aided decision support system for facial disorders.

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Reinforcement learning coupled with finite element modeling for facial motion learning

Cited by 7 publications

References 29 publications

A review: Swarm Robotics: Cooperative Control in Multi-Agent Systems

A review: Swarm Robotics: Cooperative Control in Multi-Agent Systems

Developments in Image Processing Using Deep Learning and Reinforcement Learning

Fast 3D Face Reconstruction from a Single Image Using Different Deep Learning Approaches for Facial Palsy Patients

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