# Causal Robot Communication Inspired by Observational Learning Insights

arXiv: https://arxiv.org/abs/2203.09114 · HTML: https://mlsanity.com/paper/2203.09114/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 34 references that are in the map.
- First posted on arXiv 17 Mar 2022.

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

- [Behavior Trees in Robotics and AI: An Introduction](https://mlsanity.com/paper/1709.00084/) (2017)
- [Establishing Appropriate Trust via Critical States](https://mlsanity.com/paper/1810.08174/) (2018)
- [Counterfactual States for Atari Agents via Generative Deep Learning](https://mlsanity.com/paper/1909.12969/) (2019)

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

- [Exploring Causality for HRI: A Case Study on Robotic Mental Well-being Coaching](https://mlsanity.com/paper/2503.11684/) (2025)

## Abstract (arXiv)

Autonomous robots must communicate about their decisions to gain trust and acceptance. When doing so, robots must determine which actions are causal, i.e., which directly give rise to the desired outcome, so that these actions can be included in explanations. In behavior learning in psychology, this sort of reasoning during an action sequence has been studied extensively in the context of imitation learning. And yet, these techniques and empirical insights are rarely applied to human-robot interaction (HRI). In this work, we discuss the relevance of behavior learning insights for robot intent communication, and present the first application of these insights for a robot to efficiently communicate its intent by selectively explaining the causal actions in an action sequence.