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Accelerating Interactive Reinforcement Learning by Human Advice for an Assembly Task by a Cobot

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Departement of Mechanical Engineering, Vrije Universiteit Brussel: Pleinlaan 2, 1000 Brussel, Belgium
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Flanders Make vzw, Celestijnenlaan 300, 3001 Leuven, Belgium
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Artificial Intelligence Lab, Vrije Universiteit Brussel: Pleinlaan 2, 1000 Brussel, Belgium
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Robotics 2019, 8(4), 104; https://doi.org/10.3390/robotics8040104
Received: 29 October 2019 / Revised: 11 December 2019 / Accepted: 12 December 2019 / Published: 16 December 2019
(This article belongs to the Special Issue Reinforcement Learning for Robotics Applications)
The assembly industry is shifting more towards customizable products, or requiring assembly of small batches. This requires a lot of reprogramming, which is expensive because a specialized engineer is required. It would be an improvement if untrained workers could help a cobot to learn an assembly sequence by giving advice. Learning an assembly sequence is a hard task for a cobot, because the solution space increases drastically when the complexity of the task increases. This work introduces a novel method where human knowledge is used to reduce this solution space, and as a result increases the learning speed. The method proposed is the IRL-PBRS method, which uses Interactive Reinforcement Learning (IRL) to learn from human advice in an interactive way, and uses Potential Based Reward Shaping (PBRS), in a simulated environment, to focus learning on a smaller part of the solution space. The method was compared in simulation to two other feedback strategies. The results show that IRL-PBRS converges more quickly to a valid assembly sequence policy and does this with the fewest human interactions. Finally, a use case is presented where participants were asked to program an assembly task. Here, the results show that IRL-PBRS learns quickly enough to keep up with advice given by a user, and is able to adapt online to a changing knowledge base. View Full-Text
Keywords: interactive reinforcement learning; programming by advice; assembly planning; cobots interactive reinforcement learning; programming by advice; assembly planning; cobots
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De Winter, J.; De Beir, A.; El Makrini, I.; Van de Perre, G.; Nowé, A.; Vanderborght, B. Accelerating Interactive Reinforcement Learning by Human Advice for an Assembly Task by a Cobot. Robotics 2019, 8, 104.

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