# Designs for Enabling Collaboration in Human-Machine Teaming via Interactive and Explainable Systems

arXiv: https://arxiv.org/abs/2406.05003 · HTML: https://mlsanity.com/paper/2406.05003/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jun 2024.

## Built on by (most cited first, 4 of 4)

- [Human in the Latent Loop (HILL): Interactively Guiding Model Training Through Human Intuition](https://mlsanity.com/paper/2505.06325/) (2025)
- [Mixed-Initiative Dialog for Human-Robot Collaborative Manipulation](https://mlsanity.com/paper/2508.05535/) (2025)
- [Beyond Partner Diversity: An Influence-Based Team Steering Framework for Zero-Shot Human-Machine Teaming](https://mlsanity.com/paper/2605.15400/) (2026)
- [Evaluating XAI Support From A Hierarchical Reinforcement Learning Policy in Human-Agent Collaboration](https://mlsanity.com/paper/2608.06381/) (2026)

## Abstract (arXiv)

Collaborative robots and machine learning-based virtual agents are increasingly entering the human workspace with the aim of increasing productivity and enhancing safety. Despite this, we show in a ubiquitous experimental domain, Overcooked-AI, that state-of-the-art techniques for human-machine teaming (HMT), which rely on imitation or reinforcement learning, are brittle and result in a machine agent that aims to decouple the machine and human's actions to act independently rather than in a synergistic fashion. To remedy this deficiency, we develop HMT approaches that enable iterative, mixed-initiative team development allowing end-users to interactively reprogram interpretable AI teammates. Our 50-subject study provides several findings that we summarize into guidelines. While all approaches underperform a simple collaborative heuristic (a critical, negative result for learning-based methods), we find that white-box approaches supported by interactive modification can lead to significant team development, outperforming white-box approaches alone, and that black-box approaches are easier to train and result in better HMT performance highlighting a tradeoff between explainability and interactivity versus ease-of-training. Together, these findings present three important future research directions: 1) Improving the ability to generate collaborative agents with white-box models, 2) Better learning methods to facilitate collaboration rather than individualized coordination, and 3) Mixed-initiative interfaces that enable users, who may vary in ability, to improve collaboration.