Integrated Deep Learning Architectures for Perception, Control, and Decision-making in Robotics: a Framework for Sensing, Cognition, and Transparent Decision-making

Ruchik Kashyapkumar Thaker

doi:10.36948/ijfmr.2024.v06i05.29708

Integrated Deep Learning Architectures for Perception, Control, and Decision-making in Robotics: a Framework for Sensing, Cognition, and Transparent Decision-making

Author(s)	Ruchik Kashyapkumar Thaker
Country	Canada
Abstract	The increasing complexity of robotic applications demands innovative approaches for addressing problems that lack analytical solutions, with deep learning (DL) emerging as a key tool for enabling robots to learn and adapt in dynamic environments. This survey reviews existing DL techniques in robotics, categorizing the major challenges and exploring successful solutions that leverage DL for perception, control, and decision-making. We discuss the use of modular versus end-to-end DL architectures, providing guidelines for selecting appropriate model structures and training strategies. The review also examines advancements in neuro-robotics systems (NRS), where the convergence of neuroscience and robotics is driving the development of robots with embodied intelligence, enabling more natural human-robot interactions. Recent progress in neural mechanisms for perception, cognition, learning, and control is highlighted, offering insights into creating future neuro-robots. Furthermore, we address the challenge of uncertainty estimation in neural networks, crucial for reliable robotic decision-making, and evaluate existing frameworks such as Bayesian belief networks and Monte Carlo sampling that improve uncertainty modeling without requiring architectural changes. Lastly, we explore efforts to enhance transparency in robotic systems through integrated reasoning and learning methods, focusing on architectures that combine logical reasoning with deep learning to provide explainable decision-making. This survey aims to offer a structured overview of current research and guide future developments in neuro-robotics.
Keywords	Deep learning (DL), Robotics, Perception, Control, Decision-making, Neuro-robotics systems (NRS), Embodied intelligence, Neural networks
Field	Computer > Automation / Robotics
Published In	Volume 6, Issue 5, September-October 2024
Published On	2024-10-03
Cite This	Integrated Deep Learning Architectures for Perception, Control, and Decision-making in Robotics: a Framework for Sensing, Cognition, and Transparent Decision-making - Ruchik Kashyapkumar Thaker - IJFMR Volume 6, Issue 5, September-October 2024. DOI 10.36948/ijfmr.2024.v06i05.29708
DOI	https://doi.org/10.36948/ijfmr.2024.v06i05.29708
Short DOI	https://doi.org/g8pnjc

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Integrated Deep Learning Architectures for Perception, Control, and Decision-making in Robotics: a Framework for Sensing, Cognition, and Transparent Decision-making

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