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Dylan Miller

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Jul 2026

Temporal Policy: History-Initialized Action Generation for Robotic Learning from Demonstration

Temporal Policy is introduced, a generative framework based on stochastic interpolants that formulates action generation as a temporally coupled transport problem and bypasses the computational bottleneck of independent Gaussian priors, helping enable high-frequency, closed-loop control.

Dylan Miller, Martin Jägersand · 0 citations

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