# Real-World Human-Robot Collaborative Reinforcement Learning

arXiv: https://arxiv.org/abs/2003.01156 · HTML: https://mlsanity.com/paper/2003.01156/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Mar 2020.

## Built on by (most cited first, 9 of 9)

- [Towards Effective and Interpretable Human-Agent Collaboration in MOBA Games: A Communication Perspective](https://mlsanity.com/paper/2304.11632/) (2023)
- [A Survey of Reinforcement Learning for Optimization in Automation](https://mlsanity.com/paper/2502.09417/) (2025)
- [The role of haptic communication in dyadic collaborative object manipulation tasks](https://mlsanity.com/paper/2203.01287/) (2022)
- [Enhancing team performance with transfer-learning during real-world human-robot collaboration](https://mlsanity.com/paper/2211.13070/) (2022)
- [Intelligent Framework for Human-Robot Collaboration: Dynamic Ergonomics and Adaptive Decision-Making](https://mlsanity.com/paper/2503.07901/) (2025)
- [Mapping Human-Agent Co-Learning and Co-Adaptation: A Scoping Review](https://mlsanity.com/paper/2506.06324/) (2025)
- [Formulating Reinforcement Learning for Human-Robot Collaboration through Off-Policy Evaluation](https://mlsanity.com/paper/2602.02530/) (2026)
- [Improving Human-Robot Teamwork in Urban Search and Rescue Through Episodic Memory of Prior Collaboration](https://mlsanity.com/paper/2606.18836/) (2026)
- [A Reinforcement Learning Framework for Safety-Aware Haptic Shared Control in Surgical Robotics](https://mlsanity.com/paper/2610.15422/) (2026)

## Abstract (arXiv)

The intuitive collaboration of humans and intelligent robots (embodied AI) in the real-world is an essential objective for many desirable applications of robotics. Whilst there is much research regarding explicit communication, we focus on how humans and robots interact implicitly, on motor adaptation level. We present a real-world setup of a human-robot collaborative maze game, designed to be non-trivial and only solvable through collaboration, by limiting the actions to rotations of two orthogonal axes, and assigning each axes to one player. This results in neither the human nor the agent being able to solve the game on their own. We use deep reinforcement learning for the control of the robotic agent, and achieve results within 30 minutes of real-world play, without any type of pre-training. We then use this setup to perform systematic experiments on human/agent behaviour and adaptation when co-learning a policy for the collaborative game. We present results on how co-policy learning occurs over time between the human and the robotic agent resulting in each participant's agent serving as a representation of how they would play the game. This allows us to relate a person's success when playing with different agents than their own, by comparing the policy of the agent with that of their own agent.