Abstract
This paper investigates human–robot collaboration in a novel setup: a human helps a mobile robot that can move and navigate freely in an environment. Specifically, the human helps by remotely taking over control during the learning of a task. The task is to find and collect several items in a walled arena, and Reinforcement Learning is used to seek a suitable controller. If the human observes undesired robot behavior, they can directly issue commands for the wheels through a game joystick. Experiments in a simulator showed that human assistance improved robot behavior efficacy by 30% and efficiency by 12%. The best policies were also tested in real life, using physical robots. Hardware experiments showed no significant difference concerning the simulations, providing empirical validation of our approach in practice.
Original language | English |
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Pages (from-to) | 16841-16852 |
Number of pages | 12 |
Journal | Neural Computing and Applications |
Volume | 35 |
Issue number | 23 |
Early online date | 13 Jan 2023 |
DOIs | |
Publication status | Published - Aug 2023 |
Bibliographical note
Funding Information:This research was funded by the Hybrid Intelligence Center, a 10-year program funded by the Dutch Ministry of Education, Culture and Science through the Netherlands Organization for Scientific Research ( https://www.hybrid-intelligence-centre.nl ), Grant No. 024.004.022.
Publisher Copyright:
© 2023, The Author(s).
Keywords
- Automation
- Human-in-the-loop
- Human–robot cooperation
- Mobile robots
- Reinforcement learning
- Safety