Learning Climbing Controllers for Physics-Based Characters

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dc.contributor.authorKang, Kyungwonen_US
dc.contributor.authorGu, Taehongen_US
dc.contributor.authorKwon, Taesooen_US
dc.contributor.editorZordan, Victoren_US
dc.date.accessioned2024-08-20T08:28:04Z
dc.date.available2024-08-20T08:28:04Z
dc.date.issued2024
dc.description.abstractWe propose a physics-based climbing controller that consists of two learning stages. Firstly, a hanging policy is trained to grasp holds in a natural posture. Once the policy is obtained, it is used to extract the positions of the holds, postures, and grip states, thus forming a dataset of favorable hanging poses. Subsequently, a climbing policy is trained to execute actual climbing maneuvers using this hanging state dataset. The climbing policy allows the character to move to the target location using limbs more evenly. Experiments have shown that the proposed method can effectively explore the space of good postures for climbing.en_US
dc.description.sectionheadersPosters
dc.description.seriesinformationEurographics/ ACM SIGGRAPH Symposium on Computer Animation - Posters
dc.identifier.doi10.2312/sca.20241165
dc.identifier.isbn978-3-03868-263-9
dc.identifier.pages2 pages
dc.identifier.urihttps://doi.org/10.2312/sca.20241165
dc.identifier.urihttps://diglib.eg.org/handle/10.2312/sca20241165
dc.publisherThe Eurographics Associationen_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectCCS Concepts: Computing methodologies → Physical simulation; Motion capture; Reinforcement learning
dc.subjectComputing methodologies → Physical simulation
dc.subjectMotion capture
dc.subjectReinforcement learning
dc.titleLearning Climbing Controllers for Physics-Based Charactersen_US
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