ABSTRACT: Tree fruit growers worldwide are grappling with labor shortages in crucial operations such as harvesting and pruning. The development of robotic solutions for these labor-intensive tasks has generated significant interest. However, existing efforts have proven to be excessively expensive, slow, or necessitate orchard reconfiguration for functionality.In this presentation, I introduce three alternative approaches to agriculture robot development: 1) Utilizing a cyber-physical system that facilitates human participation and machine learning for collaborative robotic apple harvesting. 2) Introducing a novel soft-growing multi-robot platform to reduce costs and enhance overall responsiveness. 3) Learning human-robot collaboration behaviors to optimize efficiency. Leveraging human-robot interaction to address existing individual shortcomings of humans and robots will enable mutual learning of behaviors to enhance overall efficiency.

BIOSKETCH: Ming Luo is currently a Flaherty assistant professor in the School of Mechanical and Materials Science, Washington State University, where he directs Mechanically Intelligent Autonomous Robotics Laboratory. He is IEEE senior member. He received the PhD. Degree in Robotic Engineering from Worcester Polytechnic Institute, Worcester, MA, USA in 2017. He received Post doc training in Stanford University during 2018~2020. His academic interests include agricultural robotics,  soft robotics, snake robot, origami robot, and haptics. His research has been funded by NSF, USDA, NIFA, and the Washington Fruit Tree Research Commission. His work has also been featured on IEEE Xplore, Reuters, The Verge, and The Wall Street Journal.