1st Edition

Designing Robot Behavior in Human-Robot Interactions




ISBN 9780367179694
Published September 25, 2019 by CRC Press
256 Pages 9 Color & 104 B/W Illustrations

USD $189.95

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Book Description

In this book, we have set up a unified analytical framework for various human-robot systems, which involve peer-peer interactions (either space-sharing or time-sharing) or hierarchical interactions. A methodology in designing the robot behavior through control, planning, decision and learning is proposed. In particular, the following topics are discussed in-depth: safety during human-robot interactions, efficiency in real-time robot motion planning, imitation of human behaviors from demonstration, dexterity of robots to adapt to different environments and tasks, cooperation among robots and humans with conflict resolution. These methods are applied in various scenarios, such as human-robot collaborative assembly, robot skill learning from human demonstration, interaction between autonomous and human-driven vehicles, etc.

Key Features:

  • Proposes a unified framework to model and analyze human-robot interactions under different modes of interactions.
  • Systematically discusses the control, decision and learning algorithms to enable robots to interact safely with humans in a variety of applications.
  • Presents numerous experimental studies with both industrial collaborative robot arms and autonomous vehicles.

Table of Contents

Table of Contents

SECTION I INTRODUCTION
 
Introduction
Human-Robot Interactions: An Overview
Modes of Interactions
Robot Behavior System
Design Objectives
System Evaluation
Outline of the Book

Framework
Multi-Agent Framework
Agent Behavior Design and Architecture
Conclusion

SECTION II THEORY

Safety during Human-Robot Interactions
Overview
Safety-Oriented Behavior Design
Safe Set Algorithm
Safe Exploration Algorithm
An Integrated Method for Safe Motion Control
Conclusion

Efficiency in Real-Time Motion Planning
Overview
Problem Formulation
Optimization-Based Trajectory Planning
Optimization-Based Speed Profile Planning
Optimization-Based Layered Planning
Conclusion

Imitation: Mimicking Human Behavior
Overview
Imitation for Prediction
Imitation for Action
Conclusion

Dexterity: Analogy Learning to Expand Robot Skill Sets
Overview
Concept of Analogy Learning
Advantages of Analogy Learning
Structure Preserved Registration for Analogy Learning
Experimental Study
Conclusion

Cooperation: Conflict Resolution during Interactions
Overview
Dynamics of Multi-Agent Systems
Cooperation under Information Asymmetry
Conflict Resolution through Communication
Conclusion


SECTION III APPLICATIONS

Human-Robot Co-existence: Space-Sharing Interactions
Overview
Robot Safe Interaction System for Industrial Robots
Robustly-Safe Automated Driving System
Conclusion

Robot Learning from Human: Hierarchical Interactions
Overview
Remote Lead Through Teaching for Implementing Imitation Learning
Robotic Grasping by Analogy Learning
Robotic Motion Re-planning by Analogy Learning
Conclusion

Human-Robot Collaboration: Time-Sharing Interactions
Human-Robot Collaboration in Manufacturing
Safe and Efficient Robot Collaborative System
Experimental Study
Conclusion

SECTION IV CONCLUSION

Vision for Future Robotics and Human-Robot Interactions
Roadmap to the Future
Conclusion of the Book

References

Index

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Author(s)

Biography

Changliu Liu is an assistant professor in the Robotics Institute at Carnegie Mellon University, where she leads the Intelligent Control Lab. She received her PhD degree from University of California at Berkeley in 2017. Her research interests include: robotics and human-robot interactions, control and motion planning, optimization and optimal control, multi-agent system and game theory, design and verification of safe intelligent systems.

Te Tang received his PhD degree from University of California at Berkeley in 2018. He joined FANUC America Corporation in 2018, and he is currently a researcher at FANUC Advanced Research Laboratory. His research interests include robotics, learning from demonstration, computer vision and their industrial applications.

Hsien-Chung Lin is a research engineer in FANUC Advanced Research Laboratory at FANUC America Corporation. Prior to joining FANUC, he received his Ph.D. degree from University of California at Berkeley in 2018. His research interests cover robotics, optimal control, human-robot interaction, learning from demonstration and motion planning.

Masayoshi Tomizuka received his PhD degree from MIT in 1974. In 1974, he joined the Mechanical Engineering Department of the University of California, Berkeley, where he currently is Cheryl and John Neerhout, Jr., Distinguished Professor. His research interests are control theory and its applications to mechatronic systems such as robots. He is a Life Fellow of ASME and IEEE, and a Fellow of IFAC. He was awarded the Rufus Oldenburger Medal (2002) and the Richard Bellman Control Heritage Award (2018).