This project envisions the future of scientific exploration as a collaborative endeavor between human scientists and autonomous robotic systems. The key challenge to materializing this vision lies in combining the expert knowledge of the scientist with the optimization capabilities of the autonomous system. The scientist brings specialized knowledge and experience to the table, while the autonomous system is capable of processing and evaluating large quantities of data. This research leverages these complementary strengths to develop a collaborative system capable of guiding scientific exploration and data collection by integrating input from scientists into an autonomous learning and planning framework. This is achieved by combining probabilistic planning with inverse reinforcement learning to integrate human input and prior knowledge into a unified optimization framework in the context of scientific exploration. The project team is validating the approach in the challenging domain of autonomous underwater ocean monitoring. This domain is particularly well suited for the testing of human-robot collaboration due to the limited communication available underwater and the necessary supervised autonomy capabilities. By integrating feedback from the human user into an algorithmic planning framework, the goal is to improve the efficiency of scientific data collection and gather data about phenomena that were previously outside the reach of scientific investigation. The use of autonomous vehicles for scientific data collection is becoming increasingly prominent; however, the research community lacks a foundational understanding of the interactions between scientists and autonomous vehicles. This work focuses on principled methods for integrating human input into algorithmic optimization techniques moving towards the goal of supervised autonomy for robots.
This project has the potential to change the way scientific data are collected through the development of a foundational framework for human-robot scientific collaboration. Such a framework is expected to have broad implications throughout the fields of human-robot interaction and artificial intelligence. The proposed research is being integrated into the robotics and computer science curriculum at both the graduate and undergraduate levels. It is also being utilized for K-12 robotics outreach programs in Los Angeles. The algorithms created in this research are transitioned to field tests and operations via ongoing collaborations with the Monterey Bay Aquarium Research Institute (MBARI) and the Southern California Coastal Ocean Observing System (SCCOOS).
