# Explainable Human-Robot Training and Cooperation with Augmented Reality

arXiv: https://arxiv.org/abs/2302.01039 · HTML: https://mlsanity.com/paper/2302.01039/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Feb 2023.

## Built on by (most cited first, 7 of 7)

- [Strategy and Skill Learning for Physics-based Table Tennis Animation](https://mlsanity.com/paper/2407.16210/) (2024)
- [6DOPE-GS: Online 6D Object Pose Estimation using Gaussian Splatting](https://mlsanity.com/paper/2412.01543/) (2024)
- [A Glimpse in ChatGPT Capabilities and its impact for AI research](https://mlsanity.com/paper/2305.06087/) (2023)
- [Learning Type-Generalized Actions for Symbolic Planning](https://mlsanity.com/paper/2308.04867/) (2023)
- [Precision and Recall Reject Curves for Classification](https://mlsanity.com/paper/2308.08381/) (2023)
- [Enabling Robots to Identify Missing Steps in Robot Tasks for Guided Learning from Demonstration](https://mlsanity.com/paper/2311.18355/) (2023)
- [Immersive Explainability: Visualizing Robot Navigation Decisions through XAI Semantic Scene Projections in Virtual Reality](https://mlsanity.com/paper/2504.00682/) (2025)

## Abstract (arXiv)

The current spread of social and assistive robotics applications is increasingly highlighting the need for robots that can be easily taught and interacted with, even by users with no technical background. Still, it is often difficult to grasp what such robots know or to assess if a correct representation of the task is being formed. Augmented Reality (AR) has the potential to bridge this gap. We demonstrate three use cases where AR design elements enhance the explainability and efficiency of human-robot interaction: 1) a human teaching a robot some simple kitchen tasks by demonstration, 2) the robot showing its plan for solving novel tasks in AR to a human for validation, and 3) a robot communicating its intentions via AR while assisting people with limited mobility during daily activities.