NRI: INT: COLLAB: Collaborative Task Planning and Learning through Language Communication in a Human-Robot Team

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When deployed in the field, robots will often encounter new situations or new tasks they don't have any knowledge or experience about. Even given sufficient knowledge, designing planners that can generate high quality plans and perform efficiently across various domains remains an open challenge. To address these issues, this project aims to empower robots to harness human expertise to acquire new knowledge and to engage humans in the loop of plan generation so that humans and robots can collectively arrive at a joint plan. The results will lead to principles and computational models for enabling effective human-robot teams that can adapt to new and changing environments and tasks, which will benefit many applications such as manufacturing, service, assistive technology, and search and rescue. This project will also provide new exciting training and education opportunities for students through research mentoring and curriculum development. This project investigates how humans and robots strive to mediate goals, world models, and plans to establish common ground for joint tasks. It will develop a computational framework that tightly links language and dialogue processing with the robot's underlying planning system to support collaborative task planning and learning in a human-robot team. It further will evaluate collaborative model acquisition and plan generation in terms of consistency of shared understanding, plan quality, and situational awareness. The research will transform planning in a human-robot team by integrating human expertise and knowledge in a collaborative process to improve planning and task performance. It will endow the robot with an ability to explain its internal states, goals and plans, and to continuously learn new states, actions, and plans through language communication with human partners. It will also advance language and dialogue research by providing a rich context for studying grounded semantics of language.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.