# Enhancing team performance with transfer-learning during real-world human-robot collaboration

arXiv: https://arxiv.org/abs/2211.13070 · HTML: https://mlsanity.com/paper/2211.13070/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 48 references that are in the map.
- First posted on arXiv 23 Nov 2022.

## Builds on (most cited first, 15 of 15)

- [Policy Distillation](https://mlsanity.com/paper/1511.06295/) (2015)
- [On the Utility of Learning about Humans for Human-AI Coordination](https://mlsanity.com/paper/1910.05789/) (2019)
- [Residual Reinforcement Learning for Robot Control](https://mlsanity.com/paper/1812.03201/) (2018)
- [Transfer Learning in Deep Reinforcement Learning: A Survey](https://mlsanity.com/paper/2009.07888/) (2020)
- [Soft Actor-Critic for Discrete Action Settings](https://mlsanity.com/paper/1910.07207/) (2019)
- [Learning to Walk via Deep Reinforcement Learning](https://mlsanity.com/paper/1812.11103/) (2018)
- [Robotic Grasping using Deep Reinforcement Learning](https://mlsanity.com/paper/2007.04499/) (2020)
- [3D Simulation for Robot Arm Control with Deep Q-Learning](https://mlsanity.com/paper/1609.03759/) (2016)
- [Evaluating Visual Conversational Agents via Cooperative Human-AI Games](https://mlsanity.com/paper/1708.05122/) (2017)
- [Deep Reinforcement learning for real autonomous mobile robot navigation in indoor environments](https://mlsanity.com/paper/2005.13857/) (2020)
- [Real-World Human-Robot Collaborative Reinforcement Learning](https://mlsanity.com/paper/2003.01156/) (2020)
- [Pretraining Deep Actor-Critic Reinforcement Learning Algorithms With Expert Demonstrations](https://mlsanity.com/paper/1801.10459/) (2018)
- [On Reward Shaping for Mobile Robot Navigation: A Reinforcement Learning and SLAM Based Approach](https://mlsanity.com/paper/2002.04109/) (2020)
- [Human-Robot Collaboration: From Psychology to Social Robotics](https://mlsanity.com/paper/1705.10146/) (2017)
- [Collaborative Agent Gameplay in the Pandemic Board Game](https://mlsanity.com/paper/2103.11388/) (2021)

## Abstract (arXiv)

Socially aware robots should be able, among others, to support fluent human-robot collaboration in tasks that require interdependent actions in order to be solved. Towards enhancing mutual performance, collaborative robots should be equipped with adaptation and learning capabilities. However, co-learning can be a time consuming procedure. For this reason, transferring knowledge from an expert could potentially boost the overall team performance. In the present study, transfer learning was integrated in a deep Reinforcement Learning (dRL) agent. In a real-time and real-world set-up, two groups of participants had to collaborate with a cobot under two different conditions of dRL agents; one that was transferring knowledge and one that did not. A probabilistic policy reuse method was used for the transfer learning (TL). The results showed that there was a significant difference between the performance of the two groups; TL halved the time needed for the training of new participants to the task. Moreover, TL also affected the subjective performance of the teams and enhanced the perceived fluency. Finally, in many cases the objective performance metrics did not correlate with the subjective ones providing interesting insights about the design of transparent and explainable cobot behaviour.