Talk: More than a Robot: Enabling Sequential Human Interaction among Multiple Robots

Abstract: Human-robot interaction in the future will not be limited to one-to-one interactions between a single robot and a single human. Increasingly complex task requirements are unlikely to be met by a single robot’s capabilities. Therefore, humans will have to interact with multiple robots and other embodied intelligences, simultaneously or sequentially, to complete tasks.

In this talk, I will describe research to broaden knowledge on a specific, crucial aspect of multi-robot human interaction: seamlessly transfering a person between multiple robots to complete a task. My work uses a combination of design, behavioral, and technical methods to advance sequential multi-robot human interaction. This new understanding of effective person transfer will inform developers and designers on appropriate robot behaviors in the future. This example also demonstrates the value of completing a design-behavior-technical research chain. This talk will first describe rationales for transfers and important aspects of transferring users. It will then explore how person transfers should be designed and implemented in both laboratory and field settings. And finally, it will describe recent work showing how robots can collaborate with not only other robots, but also with smart home systems, to enable new abilities and enhanced task efficiency.

Bio:Xiang Zhi Tan, PhD, is a postdoctoral fellow working with Prof. Sonia Chernova at the Robot Autonomy and Interactive Learning Lab at the Georgia Institute of Technology. His research looks at how robot systems and algorithms should be designed to enable seamless interaction among humans and multiple robots and across time. He received his PhD in Robotics in 2021 from Carnegie Mellon University advised by Prof. Aaron Steinfeld. He holds a Bachelor of Science degree from the University of Wisconsin–Madison. In his free time, he has been trying to figure out how to be ambidextrous.