InHuman-Robot Interaction Control Using Reinforcement Learning,an expert team of authors deliversa concise overview of human-robot interaction control schemes and insightful presentations of novel, model-free and reinforcement learning controllers. The book begins with a brief introduction to state-of-the-art human-robot interaction control and reinforcement learning before moving on to describe the typical environment model. The authors also describe some of the most famous identification techniques for parameter estimation.
Human-Robot Interaction Control Using Reinforcement Learningoffers rigorous mathematical treatments and demonstrations that facilitate the understanding of control schemes and algorithms.It also describes stability and convergence analysis of human-robot interaction control and reinforcement learning based control.
The authorsalsodiscussadvanced and cutting-edge topics, like inverse and velocity kinematics solutions, H2 neural control, and likely upcoming developments in the field of robotics.
Readers will also enjoy:

• A thorough introduction to model-based human-robot interaction control
  
• Comprehensive explorations of model-free human-robot interaction control and human-in-the-loop control using Euler angles
  
• Practical discussions of reinforcement learning for robot position and force control, as well as continuous time reinforcement learning for robot force control
  
• In-depth examinations of robot control in worst-case uncertainty using reinforcement learning and the control of redundant robots using multi-agent reinforcement learning
  

Perfect forsenior undergraduate and graduate students, academic researchers, and industrial practitioners studying and working in the fields of robotics, learning control systems, neural networks, and computational intelligence,Human-Robot Interaction Control Using Reinforcement Learningis also an indispensable resource forstudents and professionals studying reinforcement learning.

Related Downloads :

{related-news}