2018-10-23-ohnbar18a.md

title	abstract	keywords	layout	series	id	month	tex_title	firstpage	lastpage	page	order	cycles	bibtex_author	author	date	address	publisher	container-title	volume	genre	issued	pdf	extras
Personalized Dynamics Models for Adaptive Assistive Navigation Systems	Consider an assistive system that guides visually impaired users through speech and haptic feedback to their destination. Existing robotic and ubiquitous navigation technologies (e.g., portable, ground, or wearable systems) often operate in a generic, user-agnostic manner. However, to minimize confusion and navigation errors, our real-world analysis reveals a crucial need to adapt theinstructional guidance across different end-users with diverse mobility skills. To address this practical issue in scalable system design, we propose a novel model based reinforcement learning framework for personalizing the system-user interaction experience. When incrementally adapting the system to new users, we propose to use a weighted experts model for addressing data-efficiency limitations in transfer learning with deep models. A real-world dataset of navigation by blind users is used to show that the proposed approach allows for (1) more accurate long-term human behavior prediction (up to 20 seconds into the future) through improved reasoning over personal mobility characteristics, interaction with surrounding obstacles, and the current navigation goal, and (2) quick adaptation at the onset of learning, when data is limited.	Indoor Navigation, Model-Based Reinforcement Learning, Human-Robot Interaction, Assistive Technologies for the Visually Impaired	inproceedings	Proceedings of Machine Learning Research	ohnbar18a	0	Personalized Dynamics Models for Adaptive Assistive Navigation Systems	16	39	16-39	16	false	OhnBar, Eshed and Kitani, Kris and Asakawa, Chieko	given family Eshed OhnBar given family Kris Kitani given family Chieko Asakawa	2018-10-23		PMLR	Proceedings of The 2nd Conference on Robot Learning	87	inproceedings	date-parts 2018 10 23	http://proceedings.mlr.press/v87/ohnbar18a/ohnbar18a.pdf