ACC Workshop on Recent Advancement of Human Autonomy Interaction and Integration
May 24, 2021
Goals: This workshop presents recent advancement of human-autonomy interaction and integration. This session is sponsored by the technical committee on Manufacturing Automation and Robotic Control of IEEE CSS. This workshop is motivated by a healthy growth of the body of research in many areas related to systems and control, including but not limited to, design and verification of safe co-robots in an industrial environment, autonomous or assisted driving on highways, human on-the-loop control and monitoring of swarms of unmanned vehicles, and assist-as-needed control of wearable robotics. Canonical control systems design and analysis tools have found their way into solving a small subset of problems, but are still largely on a case-by-case basis. Research in this domain require a good balance between model-based methods and learning-based methods, and between fully autonomy and human guidance. Major challenges naturally arise in addressing the lack of first principles and proven mathematical models for human intentions and human behaviors. We have envisioned that the control community will play a more crucial role in human autonomy interaction and integration by taking on challenges including: joint decision making, verifiable safety, learning based adaptation, workload and comfort management, trust calibration and control. We aim to report recent research achievements and identify these relevant challenges, as well as a review for testbed and facilities for this domain of research.
We have organized a full-day workshop on human robot interaction for ACC 2017. Since then, there have been significant advancements on human autonomy interaction and integration. The field of human robot interaction and cyber physical human systems is booming with several dedicated workshops and invited sessions appeared in major control and robotics conferences. This ACC 2021 workshop will focus more on autonomous systems with human in/on the loop based on integration of classical control theories with recent progress in learning, human factor research, as well as ergonomic designs.
Neera Jain, Purdue University
Changliu Liu, Carnegie Mellon University
Shaoshuai Mou, Purdue University
Yue Wang, Clemson University
Fumin Zhang, Georgia Tech
Wenlong Zhang, Arizona State University