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Flow-Matched Motion Priors (FMP) is introduced, an online scalar reward learned from paths connecting current rollout histories to an expert motion bank, and Controlled reward-model experiments show substantially better generalization beyond the fitting rollout than value-only or endpoint-only fitting.
Trajectory optimization just got a whole lot faster and more energy-efficient: a GPU-native solver achieves 4x speedup and halves energy consumption compared to optimized CPU baselines.