Machine Learning for Cognitive Robotics shows how advances in deep learning are reshaping robotics into systems that can see, learn, and act with greater autonomy. Drawing on breakthroughs in computer vision, natural language processing, and tactile sensing, it explains how neural networks allow robots to recognize objects, adapt their behavior, and interact more naturally with people and environments.

This book is book 9 of the series 'Cognitive Robotics' .

The book explores core approaches such as convolutional and recurrent networks, multimodal learning, and reinforcement learning, highlighting how these methods translate into capabilities like grasping, imitation, and communication. By bridging theory with real-world applications, it offers a clear roadmap for understanding how machine learning powers the next generation of intelligent robots.

Accessible yet comprehensive, Machine Learning for Cognitive Robotics is an essential resource for anyone interested in artificial intelligence, robotics, and the future of autonomous machines.