Towards Continual Reinforcement Learning for Quadruped Robots

dc.contributor.authorMinelli, Giovannien_US
dc.contributor.authorVassiliades, Vassilisen_US
dc.contributor.editorPelechano, Nuriaen_US
dc.contributor.editorLiarokapis, Fotisen_US
dc.contributor.editorRohmer, Damienen_US
dc.contributor.editorAsadipour, Alien_US
dc.date.accessioned2023-10-02T08:17:25Z
dc.date.available2023-10-02T08:17:25Z
dc.date.issued2023
dc.description.abstractQuadruped robots have emerged as an evolving technology that currently leverages simulators to develop a robust controller capable of functioning in the real-world without the need for further training. However, since it is impossible to predict all possible real-world situations, our research explores the possibility of enabling them to continue learning even after their deployment. To this end, we designed two continual learning scenarios, sequentially training the robot on different environments while simultaneously evaluating its performance across all of them. Our approach sheds light on the extent of both forward and backward skill transfer, as well as the degree to which the robot might forget previously acquired skills. By addressing these factors, we hope to enhance the adaptability and performance of quadruped robots in real-world scenarios.en_US
dc.description.sectionheadersNovel Technologies for Digital Avatars and Animation
dc.description.seriesinformationInternational Conference on Interactive Media, Smart Systems and Emerging Technologies (IMET)
dc.identifier.doi10.2312/imet.20231258
dc.identifier.isbn978-3-03868-233-2
dc.identifier.pages61-64
dc.identifier.pages4 pages
dc.identifier.urihttps://doi.org/10.2312/imet.20231258
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/imet20231258
dc.publisherThe Eurographics Associationen_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.titleTowards Continual Reinforcement Learning for Quadruped Robotsen_US
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