# Towards Preference Learning for Autonomous Ground Robot Navigation Tasks

arXiv: https://arxiv.org/abs/2010.16361 · HTML: https://mlsanity.com/paper/2010.16361/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Oct 2020.

## Built on by (most cited first, 1 of 1)

- [AI-HRI Brings New Dimensions to Human-Aware Design for Human-Aware AI](https://mlsanity.com/paper/2210.11832/) (2022)

## Abstract (arXiv)

We are interested in the design of autonomous robot behaviors that learn the preferences of users over continued interactions, with the goal of efficiently executing navigation behaviors in a way that the user expects. In this paper, we discuss our work in progress to modify a general model for robot navigation behaviors in an exploration task on a per-user basis using preference-based reinforcement learning. The novel contribution of this approach is that it combines reinforcement learning, motion planning, and natural language processing to allow an autonomous agent to learn from sustained dialogue with a human teammate as opposed to one-off instructions.