CAREER: When Reality Fails Expectations: Containing Reflective Domain Models for Human-Aware Planning and Learning of Robotic Teammates

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This Faculty Early Career Development (CAREER) program will introduce a transformative paradigm, referred to as Reflective Robotics, for planning and learning of robotic teammates. Despite significant research into the development of autonomous robotic agents, humans are still experiencing ambivalence towards their robotic teammates. The problem lies not only in the limitations of robots to interacting with humans, but also in the humans’ inability to understand their robotic partners. Humans tend to be overly optimistic or pessimistic towards robots, resulting in an unforgiving discordance between reality and expectations and, ultimately, egregious teaming failures. This study will address a fundamental cause for such a discordance due to different understandings of the task domain between the human and the robot. In such situations, it is crucial for the robot to understand how to reconcile the discrepancy to maintain proper teaming. The framework to expand the applicability of robotic technologies aligns with the broad call for co-robot development. It represents a key enabler of interpretable and safe robots for human-robot interaction. The educational goal is to innovate robotics education to excite and attract students, train the next generation of scientists and engineers in human-robot collaboration, engage the public audience in the discussion, and boost trust in robotics technologies. The activities will benefit K-12 students, undergraduate and graduate students, and students from underrepresented groups.The specific research goal is to address the different understandings of the task domain between the human and the robot, referred to as reflective domain models, by developing model reflective planning and learning methods to form the theoretical and algorithmic foundation of Reflective Robotics. The key to containing such reflective domain models is for the robot to maintain estimates of the true domain model and the human’s understanding of it and utilize both to inform its operation. In particular, 1) Model Reflective Planning contributes uniquely to the paradigm shift in planning to generalize planning methods to open-world domains for real-world applications. In contrast to the traditional planning methods that depend only on a single domain model, model reflective planning also considers the human’s understanding of it. A Bayesian approach is to actively model such an understanding while incorporating the hierarchical information as state and action abstractions to inform planning methods. As such, model reflective planning contributes to the realization of real-world autonomy for robotic teammates. 2) Model Reflective Learning advocates a paradigm shift in the design of learning methods to generalize to situations with reflective models, which are the norm rather than the exceptions with non-expert users. A framework based on entropy augmented reinforcement learning integrated with variational inference will be investigated. It avoids a pitfall by addressing a critical gap in the existing learning systems where human users can mislead learning in non-trivial and systematic ways. At the same time, these results will provide an exciting new foundation for the PI’s long-term career in robotics and generate new growth and leadership opportunities in the community.This project is supported by the cross-directorate Foundational Research in Robotics program, jointly managed and funded by the Directorates for Engineering (ENG) and Computer and Information Science and Engineering (CISE).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.