March 8, 2017

Autonomous Robotics and Deep Learning by Vishnu Nath, Stephen E. Levinson (auth.)

By Vishnu Nath, Stephen E. Levinson (auth.)

This Springer short examines the combo of machine imaginative and prescient ideas and desktop studying algorithms important for humanoid robots to enhance “true consciousness.” It illustrates the severe first step in the direction of attaining “deep learning,” lengthy thought of the holy grail for computer studying scientists around the globe. utilizing the instance of the iCub, a humanoid robotic which learns to resolve 3D mazes, the publication explores the demanding situations to create a robotic which can understand its personal atmosphere. instead of depending exclusively on human programming, the robotic makes use of actual contact to strengthen a neural map of its setting and learns to alter the surroundings for its personal gain. those concepts let the iCub to properly remedy any maze, if an answer exists, inside a couple of iterations. With transparent research of the iCub experiments and its effects, this Springer short is perfect for complex point scholars, researchers and pros interested in desktop imaginative and prescient, AI and computer learning.

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Extra resources for Autonomous Robotics and Deep Learning

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While creating an open hardware and software platform in humanoid robotics is one of the goals of the RobotCub, the primary goal of the RobotCub project is to advance the current understanding of natural and artificial cognitive systems (Metta et al. 2008). 41 ft) tall, the iCub is the size of a three and half year old child. The iCub is able to perform a variety of physical feats like crawling on all fours, grasp small objects like balls, etc. (Nath and Levinson 2013a, b). RobotCub’s stance on cognition is that manipulation of objects by an agent plays a fundamental role in the development of its cognitive ability (Metta et al.

2. As aforementioned, unsupervised learning primarily includes clustering because of the lack of any labels in the data. There are other methods that also employ unsupervised learning like hidden Markov models. A detailed discussion of these topics is beyond the scope of this book and is not discussed. Supervised learning, on the other hand, creates a mapping function from input to output that is constantly being improvised by the labeled training data. Supervised learning has a lot of applications in classification and in regression applications.

A deeper discussion of eigenvalues and eigenvectors is not required for the understanding of the material presented in this book and is being omitted here for the sake of brevity. References Barber, D. (2012). Bayesian Reasoning and Machine Learning. Cambridge: University Press. , & Levinson, S. (2013a). Learning to Fire at Targets by an iCub Humanoid Robot. AAAI Spring Symposium. Palo Alto: AAAI. , & Levinson, S. (2013b). Usage of computer vision and machine learning to solve 3D mazes. Urbana: University of Illinois at Urbana-Champaign.

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