Presentation
Learning Active Quasistatic Physics-based Models From Data
SessionTechnical Papers
Event Type
Technical Paper
Research & Education
Ultimate Supporter
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DescriptionWe design a differentiable quasistatic simulator to learn a low-dimensional control space for active models, reconstruct training poses, generalize to unseen poses, and enable further physics-based edits on the output.
