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.
- 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
Human-Robot Interactions: An Overview
Modes of Interactions
Robot Behavior System
Outline of the Book
Agent Behavior Design and Architecture
SECTION II THEORY
Safety during Human-Robot Interactions
Safety-Oriented Behavior Design
Safe Set Algorithm
Safe Exploration Algorithm
An Integrated Method for Safe Motion Control
Efficiency in Real-Time Motion Planning
Optimization-Based Trajectory Planning
Optimization-Based Speed Profile Planning
Optimization-Based Layered Planning
Imitation: Mimicking Human Behavior
Imitation for Prediction
Imitation for Action
Dexterity: Analogy Learning to Expand Robot Skill Sets
Concept of Analogy Learning
Advantages of Analogy Learning
Structure Preserved Registration for Analogy Learning
Cooperation: Conflict Resolution during Interactions
Dynamics of Multi-Agent Systems
Cooperation under Information Asymmetry
Conflict Resolution through Communication
SECTION III APPLICATIONS
Human-Robot Co-existence: Space-Sharing Interactions
Robot Safe Interaction System for Industrial Robots
Robustly-Safe Automated Driving System
Robot Learning from Human: Hierarchical Interactions
Remote Lead Through Teaching for Implementing Imitation Learning
Robotic Grasping by Analogy Learning
Robotic Motion Re-planning by Analogy Learning
Human-Robot Collaboration: Time-Sharing Interactions
Human-Robot Collaboration in Manufacturing
Safe and Efficient Robot Collaborative System
SECTION IV CONCLUSION
Vision for Future Robotics and Human-Robot Interactions
Roadmap to the Future
Conclusion of the Book
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).