NRI: FND: Probabilistic Hypothesis-Driven Adaptive Human-Robot Teams

点击翻译按钮获取中文摘要

Teams of humans and robots working together have the potential to transform search and rescue operations, given the maturation of robotic technology and the ability to task each (human or robot) individually and collaboratively. Example applications include locating survivors or gas leaks after earthquakes or fires, where the safe access for first responders is limited, and locating a chemical/biological release in an urban environment. The disaster response example offers a broad motivational challenge, given the potentially hazardous situations for humans, trapped survivors, fast changing conditions, and communication uncertainties. The key challenge in developing truly efficient and high performing human-robot teams is enabling the team to intelligently reason and act together. This research project will address this challenge by developing hypothesis-based methods to communicate between humans and robots to enable their collective perception about the environment, and to develop sub-teams and tasks to address the search problem as it evolves. Broader educational impacts include having undergraduate and high school students collaborate with the research team to perform experiments and data collection. Outcomes include open source algorithms and data logs for researchers in the community, and for robotics and controls classes; dissemination through publications, conferences, workshops and NRI meetings; and the inclusion of undergrad and high school students and diversity programs collaborating in the interdisciplinary area of human-robot teaming.The goal of this research project is to develop foundational theory and validated algorithms for human-robot teams which can uniquely operate in, and adapt to, a complex and changing environment, particularly as knowledge of the environment/tasks evolves over time. The technical approach uses a probabilistic hypothesis formulation as a basis to formulate both the Process Inference and Team Forming problems. Formal modeling methods will connect human's natural language to hypotheses of the perception and teaming tasks, thereby allowing humans and robots to communicate efficiently and collaboratively. Hypotheses will be evaluated by the robots for information content using physical and data driven models to capture the processes and sensing. Importantly, both the inference and teaming will evolve as the complex processes evolve. The hypothesis-based approaches and team adaptation will be validated in a series of human-robot search experiments, and scaling will be validated via large scale simulations. The approach also enables the perception and planning to evolve as the scene evolves. This project aims to advance cooperative robot collaboration via human-robot information exchange; the scalability of cooperative robot teams where the team itself adapts over time as information is collected and knowledge is formed; and demonstrate the role for physical embodiment of intelligent systems as human-robot teams address complex applicable models.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.