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- W3090087065 abstract "Learning Assistance by Demonstration (LAD) is concerned with using demonstrations of a human agent to teach a robot how to assist another human. The concept has previously been used with smart wheelchairs to provide customised assistance to individuals with driving difficulties. A basic premise of this technique is that the learned assistive policy should be able to generalise to environments different than the ones used for training; but this has not been tested before. In this work we evaluate the assistive power and the generalisation capability of LAD using our custom teleoperation and learning system for smart wheelchairs, while seeking to improve it by experimenting with different combinations of dimensionality reduction techniques and machine learning models. Using Autoencoders to reduce the dimension of laserscan data and a Gaussian Process as the learning model, we achieved a 23% improvement in prediction performance against the combination used by the latest work on the field. Using this model to assist a driver exposed to a simulated disability, we observed a 9.8% reduction in track completion times when compared to driving without assistance." @default.
- W3090087065 created "2020-10-08" @default.
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- W3090087065 date "2020-05-01" @default.
- W3090087065 modified "2023-09-25" @default.
- W3090087065 title "Improving Generalisation in Learning Assistance by Demonstration for Smart Wheelchairs" @default.
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- W3090087065 doi "https://doi.org/10.1109/icra40945.2020.9197490" @default.
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