CAREER: Learning and Leveraging Conventions in the Design of an Adaptive Haptic Shared Control for Steering a Semi-Automated Vehicle

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Humans may gravitate to different strategies for resolving a conflict. However, current solutions for control transfer in semi-automated vehicles are mainly designed based on predefined rules and do not personalize the automation's strategies for resolving a conflict. As a result, these solutions face issues such as prolonged transfer time and misinterpretations or misappropriations of responsibility. A hypothesis behind the seamless human-human collaboration is that humans can adaptively form conventions. A convention is defined as shared representations that capture the interaction and can change over time. However, forming conventions in humans-robots teams is difficult because the human partner is a non-stationary agent. In this Faculty Early Career Development (CAREER) project, the plan is to design and test adaptable and convention-based control transfer strategies to enhance joint driving performance and subjective assessment of driving. To this end, two research objectives are defined for this project. The first objective focuses on learning different forms of conventions between humans and the automation system. A modular structure that separates partner-specific conventions from task-dependent representations will be created and used to learn different forms using Bayesian-based optimization approaches. Furthermore, a map from the space of conventions to outcomes in human-machine collaboration will be characterized. The second objective focuses on developing algorithms for automation systems using multi-objective Bayesian optimization so that complex interaction policies can be learned and a desirable convention between a human and an automation system can be achieved. The effectiveness of the platform will be validated through a series of case studies with human-subject participants in the loop using a haptic steering wheel driving simulator and a ground vehicle.The overarching research objective of this CAREER grant is to further enable collaborative partnerships between teams of humans and robots. Given that both humans and robots are subject to faults, the hand-off problem – how to exchange control between a human and robot— plays a critical role in ensuring the performance of a human-robot teaming. However, balancing the driver's preference and the joint task's safety in a haptic shared control may result in several possible handover strategies. While humans seamlessly resolve conflicts by co-adapting to each other, co-adaptation between humans and robots is quite challenging. This project aims to develop the principles of dynamic co-adaptation in a haptic shared control framework wherein both a human driver and an automation system collaboratively control a semi-automated ground vehicle's steering. The educational goal is to equip students and the future workforce with the technical knowledge for designing the next generation of human-machine systems through several activities, including integrating research and teaching activities and promoting STEM education for minority students and K-12 students.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.