Sammendrag
With the ongoing advancements in robotics and autonomous systems, the magnitude of unstructured data has gotten excessively enormous, while conventional data processing procedures lack successful adaptation. Besides, breaking down perplexing, high dimensional, and noisy data is a colossal challenge, emphasising the urgency of creating novel approaches that can produce a justifiable structure. To address these issues, deep learning models have yielded exceptional outcomes in the late decade.
Deep learning has transfigured the evolution of robotics by setting new horizons. The capacity of deep neural networks to represent hierarchical features from multimodal sensory data, including image, audio, text, etc., make them ground-breaking in a plethora of related tasks. Meanwhile, under no human supervision, they succeed in carrying out complex, noisy and dynamic tasks, rendering them suitable for intelligent behaviours applicable to autonomous and cognitive robotics. Hence, deep learning has significantly supported a wide variety of robotics domains, including human-computer/robot interaction, by efficiently overcoming existing barriers and announcing further issues and solutions in more advanced challenges. A wide variety of research is being conducted to explore and discover possible challenges and opportunities to exploit deep learning schemes in robotics. The current Special Issue is focused on research ideas, articles and experimental studies related to “Deep Learning and Robotics” for learning, analysing and forecasting the various aspects of deep learning in robotics applications.
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