Designing Robot Behavior in Human-Robot Interactions: 1st Edition (Hardback) book cover

Designing Robot Behavior in Human-Robot Interactions

1st Edition

By Changliu Liu, Te Tang, Hsien-Chung Lin, Masayoshi Tomizuka

CRC Press

236 pages | 9 Color Illus. | 104 B/W Illus.

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Hardback: 9780367179694
pub: 2019-09-12
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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 in the process of 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:

  • Propose a unified framework to model and analyze human-robot interactions under different modes of interactions
  • Systematically discuss the control, decision and learning algorithms to enable robot to interact safely with humans in a variety of applications
  • Present 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

About the Authors

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 currently he is 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).

Subject Categories

BISAC Subject Codes/Headings:
COM037000
COMPUTERS / Machine Theory
COM059000
COMPUTERS / Computer Engineering
SCI086000
SCIENCE / Life Sciences / General
TEC037000
TECHNOLOGY & ENGINEERING / Robotics