Imperial College London
Portrait Picture

Professor Aldo Faisal

Faculty of Engineering, Department of Bioengineering

Professor of AI & Neuroscience
 
 
 
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Contact

 

+44 (0)20 7594 6373a.faisal Website

 
 
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Assistant

 

Miss Teresa Ng +44 (0)20 7594 8300

 
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Location

 

4.08Royal School of MinesSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@unpublished{Shafti:2020,
author = {Shafti, A and Tjomsland, J and Dudley, W and Faisal, AA},
publisher = {arXiv},
title = {Real-world human-robot collaborative reinforcement learning},
url = {http://arxiv.org/abs/2003.01156v1},
year = {2020}
}
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RIS format (EndNote, RefMan)

TY  - UNPB
AB  - The intuitive collaboration of humans and intelligent robots (embodied AI) inthe real-world is an essential objective for many desirable applications ofrobotics. Whilst there is much research regarding explicit communication, wefocus 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 limitingthe actions to rotations of two orthogonal axes, and assigning each axes to oneplayer. This results in neither the human nor the agent being able to solve thegame on their own. We use a state-of-the-art reinforcement learning algorithmfor the robotic agent, and achieve results within 30 minutes of real-worldplay, without any type of pre-training. We then use this system to performsystematic experiments on human/agent behaviour and adaptation when co-learninga policy for the collaborative game. We present results on how co-policylearning occurs over time between the human and the robotic agent resulting ineach participant's agent serving as a representation of how they would play thegame. This allows us to relate a person's success when playing with differentagents than their own, by comparing the policy of the agent with that of theirown agent.
AU  - Shafti,A
AU  - Tjomsland,J
AU  - Dudley,W
AU  - Faisal,AA
PB  - arXiv
PY  - 2020///
TI  - Real-world human-robot collaborative reinforcement learning
UR  - http://arxiv.org/abs/2003.01156v1
UR  - http://hdl.handle.net/10044/1/78654
ER  -
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