# Towards Probabilistic Planning of Explanations for Robot Navigation

arXiv: https://arxiv.org/abs/2411.05022 · HTML: https://mlsanity.com/paper/2411.05022/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 21 references that are in the map.
- First posted on arXiv 26 Oct 2024.

## Builds on (most cited first, 3 of 3)

- [Bridging the Gap: Providing Post-Hoc Symbolic Explanations for Sequential Decision-Making Problems with Inscrutable Representations](https://mlsanity.com/paper/2002.01080/) (2020)
- [Towards Explainable AI Planning as a Service](https://mlsanity.com/paper/1908.05059/) (2019)
- [Explain yourself! Effects of Explanations in Human-Robot Interaction](https://mlsanity.com/paper/2204.04501/) (2022)

## Abstract (arXiv)

In robotics, ensuring that autonomous systems are comprehensible and accountable to users is essential for effective human-robot interaction. This paper introduces a novel approach that integrates user-centered design principles directly into the core of robot path planning processes. We propose a probabilistic framework for automated planning of explanations for robot navigation, where the preferences of different users regarding explanations are probabilistically modeled to tailor the stochasticity of the real-world human-robot interaction and the communication of decisions of the robot and its actions towards humans. This approach aims to enhance the transparency of robot path planning and adapt to diverse user explanation needs by anticipating the types of explanations that will satisfy individual users.