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FOCUS: First-Order Control Using Optimal Transport Supervision for Imitation from Observations
Imitation learning from observations must recover behavior from state-only demonstrations without known temporal correspondence. Differentiable simulators provide pathwise policy gradients, but differentiating through long, contact-rich trajectories can make optimization unstable. We introduce FOCUS
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