A non-asymptotic bound on compression error for signal parameterizations derived from differences in predictions at varying compression levels, verified empirically across fitting and inverse problems.
Nerf in robotics: A survey
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MorphIt is a gradient-based spherical approximation framework for robot morphology that provides tunable control over accuracy-efficiency tradeoffs and outperforms baselines in speed and geometric fidelity.
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Bounding Global and Local Compression Error of Signal Parameterizations
A non-asymptotic bound on compression error for signal parameterizations derived from differences in predictions at varying compression levels, verified empirically across fitting and inverse problems.
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MorphIt: Flexible Spherical Approximation of Robot Morphology for Representation-driven Adaptation
MorphIt is a gradient-based spherical approximation framework for robot morphology that provides tunable control over accuracy-efficiency tradeoffs and outperforms baselines in speed and geometric fidelity.