Report
A regression robot policy reportedly rivals generative baselines with faster inference
The researchers link the gap to imbalanced gradients under mean-squared-error training and propose an alternative regression method.
TLDR
In an October 2026 paper, researchers say standard mean-squared-error training produces larger gradients for observations with large action-prediction errors. Their alternative, HT-Policies, learns how error scales vary by input and predicts actions in one feed-forward pass. They report success rates competitive with generative policy baselines across four simulation benchmarks and real-robot evaluations, with faster training and inference.
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