1 research outputs found
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 the instructional 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.Comment: Oral Presentation in 2nd Conference on Robot Learning (CoRL, 2018