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Goal Blending for Responsive Shared Autonomy in a Navigating Vehicle.
Yu-Sian Jiang, Garrett
Warnell, and Peter Stone.
In Proceedings of the 35th AAAI Conference
on Artificial Intelligence (AAAI), Feb 2021.
[PDF]911.3kB [poster.pdf]1.1MB
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.
@InProceedings{AAAI21-sharonj,
author = {Yu-Sian Jiang and Garrett Warnell and Peter Stone},
title = {Goal Blending for Responsive Shared Autonomy in a Navigating Vehicle},
booktitle = {Proceedings of the 35th AAAI Conference on Artificial Intelligence (AAAI)},
location = {A Virtual Conference},
month = {Feb},
year = {2021},
abstract = {
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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