# Personalized Dynamics Models for Adaptive Assistive Navigation Systems

arXiv: https://arxiv.org/abs/1804.04118 · HTML: https://mlsanity.com/paper/1804.04118/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Apr 2018.

## Built on by (most cited first, 13 of 13)

- [Predicting the Future from First Person (Egocentric) Vision: A Survey](https://mlsanity.com/paper/2107.13411/) (2021)
- [Egocentric Prediction of Action Target in 3D](https://mlsanity.com/paper/2203.13116/) (2022)
- [PALM: Predicting Actions through Language Models](https://mlsanity.com/paper/2311.17944/) (2023)
- [Egocentric Human Trajectory Forecasting with a Wearable Camera and Multi-Modal Fusion](https://mlsanity.com/paper/2111.00993/) (2021)
- [DRAGON: A Dialogue-Based Robot for Assistive Navigation with Visual Language Grounding](https://mlsanity.com/paper/2307.06924/) (2023)
- [Untrimmed Action Anticipation](https://mlsanity.com/paper/2202.04132/) (2022)
- [MIRROR: Differentiable Deep Social Projection for Assistive Human-Robot Communication](https://mlsanity.com/paper/2203.02877/) (2022)
- [Unified Recurrence Modeling for Video Action Anticipation](https://mlsanity.com/paper/2206.01009/) (2022)
- [Text to Blind Motion](https://mlsanity.com/paper/2412.05277/) (2024)
- [Latent Embedding Adaptation for Human Preference Alignment in Diffusion Planners](https://mlsanity.com/paper/2503.18347/) (2025)
- [Domain Generalization using Action Sequences for Egocentric Action Recognition](https://mlsanity.com/paper/2506.17685/) (2025)
- [Online No-regret Model-Based Meta RL for Personalized Navigation](https://mlsanity.com/paper/2204.01925/) (2022)
- [Time-Aware Assistive Navigation](https://mlsanity.com/paper/2609.05596/) (2026)

## Abstract (arXiv)

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.