Human-robot shared autonomy techniques for vehicle navigation hold promise for reducing a human driver's workload, ensuring safety, and improving navigation efficiency. However, because typical techniques achieve these improvements by effectively removing human control at critical moments, these approaches often exhibit poor responsiveness to human commands--- especially in cluttered environments. In this paper, we propose a novel goal-blending shared autonomy (GBSA) system, which aims to improve responsiveness in shared autonomy systems by blending human and robot input during the selection of local navigation goals as opposed to low-level motor (servo-level) commands. We validate the proposed approach by performing a human study involving an intelligent wheelchair and compare GBSA to a representative servo-level shared control system that uses a policy-blending approach. The results of both quantitative performance analysis and a subjective survey show that GBSA exhibits significantly better system responsiveness and induces higher user satisfaction than the existing approach.

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In Proceedings of the 35th AAAI Conference on Artificial Intelligence (AAAI), A Virtual Conference, February 2021.

@inproceedings{AAAI21-sharonj, title={Goal Blending for Responsive Shared Autonomy in a Navigating Vehicle}, author={Yu-Sian Jiang and Garrett Warnell and Peter Stone}, booktitle={Proceedings of the 35th AAAI Conference on Artificial Intelligence (AAAI)}, month={February}, address={A Virtual Conference}, url="http://www.cs.utexas.edu/users/ai-lab?AAAI21-sharonj", year={2021} }