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dc.contributor.authorNaderi, Kouroshen_US
dc.contributor.authorBabadi, Aminen_US
dc.contributor.authorHämäläinen, Perttuen_US
dc.contributor.editorThuerey, Nils and Beeler, Thaboen_US
dc.date.accessioned2018-07-23T10:06:56Z
dc.date.available2018-07-23T10:06:56Z
dc.date.issued2018
dc.identifier.issn1467-8659
dc.identifier.urihttps://doi.org/10.1111/cgf.13513
dc.identifier.urihttps://diglib.eg.org:443/handle/10.1111/cgf13513
dc.description.abstractWe propose a novel learning-based solution for motion planning of physically-based humanoid climbing that allows for fast and robust planning of complex climbing strategies and movements, including extreme movements such as jumping. Similar to recent previous work, we combine a high-level graph-based path planner with low-level sampling-based optimization of climbing moves. We contribute through showing that neural network models of move success probability, effortfulness, and control policy can make both the high-level and low-level components more efficient and robust. The models can be trained through random simulation practice without any data. The models also eliminate the need for laboriously hand-tuned heuristics for graph search. As a result, we are able to efficiently synthesize climbing sequences involving dynamic leaps and one-hand swings, i.e. there are no limits to the movement complexity or the number of limbs allowed to move simultaneously. Our supplemental video also provides some comparisons between our AI climber and a real human climber.en_US
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.subjectComputing methodologies
dc.subjectSearch methodologies
dc.subjectMotion path planning
dc.subjectMachine learning approaches
dc.titleLearning Physically Based Humanoid Climbing Movementsen_US
dc.description.seriesinformationComputer Graphics Forum
dc.description.sectionheadersHumans
dc.description.volume37
dc.description.number8
dc.identifier.doi10.1111/cgf.13513
dc.identifier.pages69-80


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  • 37-Issue 8
    ACM SIGGRAPH / Eurographics Symposium on Computer Animation 2018

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