FW-HTF-RL/Collaborative Research: Elevating Farm Worker-Robot Collaborations in Agri-Food Ecosystems

University Of California - Merced, Merced CA

Investigators

Abstract

This Future of Work at the Human-Technology Frontier - Research: Large (FW-HTF-RL) project advances the agricultural workforce and automation technology partnership in the context of future precision farming for fresh fruit tree-crop harvesting (that is, picking and handling fruits that are meant to be sold in a store). The overarching goal of this project is to shape the future farm workplace in which human-aware agricultural robots operate in a seamless partnership with farmworkers to improve future tree-crop harvesting outcomes while improving the job experience and enhancing the productivity of food production processes. Not all tasks in fresh fruit tree-crop harvesting can be automated, and some tasks might be better offloaded to a future robotic co-worker when doing so would augment farmworker efficiency and improve the quality of work. The project brings together experts from Engineering, Computer Science, Social Science, Environmental Science, and Crop Production Management to discover these new agricultural robotics and farmworker interactions. The team aims to create scientific and technological foundations of future agricultural robotics and automation technology developed for and validated by future farmworkers and farm owners. This human worker validation will increase trust and adoption toward future precision farming and understand the implications of this technology’s integration in future agriculture workforce relations. The project investigates the deployment of pervasive, intelligent, and autonomous agricultural robotics at the frontier of the farming workforce and agricultural robotics and automation technology by creating new, expanded, and unique user-centered frameworks. The project uniquely innovates along five fundamental agricultural robotics and automation technology and agricultural workforce research directions. 1) Novel principles to co-design actuation and perception for safe, reliable, and efficient robotic harvesters. 2) Effective machine vision mechanisms to understand farmworker activities in harvesting. 3) Efficient robot planning techniques cognizant of human activities. 4) Participatory design approach for precision farming technology trust and adoption. 5) Advancement of human-robot multitasking toward sustainable agriculture. The project actively engages stakeholders (farmworkers, farm owners, packing house specialists) to assess current standards and practices and then integrate feedback after in-field demonstrations to inform iterative modifications of devices and systems. Taken together, these research directions will help extend human-robot collaboration with multitasking, explicitly exploring the trade-offs between harvesting efficiency and sustainable precision farming while shedding light on the yet-to-be-explored implications of future agriculture robotics technology on future agriculture workforce, notably as it may disrupt current compensation schemes in relation to technology ownership which in turn can further affect the degree of adoption and trust in automation. The rich set of engaging problems will provide abundant research opportunities for a diverse cohort of undergraduate students. The project integrates existing efforts in K-12 outreach events hosted at the project’s three collaborating sites – University of California (UC) Riverside, UC Merced, and UC Davis – to broaden the participation of under-represented minority groups. This project has been funded by the Future of Work at the Human-Technology Frontier cross-directorate program to promote a deeper fundamental understanding of the interdependent human-technology partnership in work contexts by advancing the design of intelligent work technologies that operate in harmony with human workers. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.