Chandigarh University (CU) Blog - Best University in India

 Soft robotics stands at the forefront of interdisciplinary innovation, blending elements of engineering, computer science, and material science to reshape various industries, spanning from healthcare to manufacturing. These robots, primarily constituted of flexible and malleable materials, boast unique attributes like adaptability to intricate surroundings and safe human interaction. Nevertheless, the optimization of their sensory perception and control remains a paramount challenge, especially within dynamic and unstructured environments. Recent years have witnessed the rise of machine learning methodologies aimed at sensor data fusion, offering a potent solution to elevate the efficacy of soft robots. Through the amalgamation of data-centric approaches with conventional control mechanisms, researchers are pushing the boundaries of what these robots can accomplish. Let's explore the transformative influence of machine learning on the domain of soft robotics: Refining Perception: ...

Machine Learning, nestled within the expansive realm of Artificial Intelligence (AI), serves as a beacon of innovation, empowering machines to glean insights from data sans explicit programming. With a focus on crafting algorithms and models that refine their performance through experience rather than instruction, this transformative technology propels automation, optimization, and data-driven decision-making across a myriad of sectors, ushering in an era of heightened efficiency, cost reduction, and enhanced outcomes. Unpacking the Core Components of Machine Learning Before immersing ourselves in the profound impact of Machine Learning on various industries, let's embark on a brief exploration of its foundational elements: Data : The lifeblood of machine learning, data fuels algorithms and models, whether structured like spreadsheets and databases or unstructured, encompassing text, images, and videos. Algorithms : Diverse machine learning algorithms, from supervised and unsupervi...