BEACON uses discrepancy-aware importance reweighting to jointly train diffusion-based robot policies and source sample weights, improving performance over target-only and fixed-ratio baselines in cross-domain manipulation tasks.
Cover and Peter E
2 Pith papers cite this work. Polarity classification is still indexing.
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Supervised UMAP works well for classification but shows clear limitations in incorporating response information for regression tasks.
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BEACON: Cross-Domain Co-Training of Generative Robot Policies via Best-Effort Adaptation
BEACON uses discrepancy-aware importance reweighting to jointly train diffusion-based robot policies and source sample weights, improving performance over target-only and fixed-ratio baselines in cross-domain manipulation tasks.
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A Comparative Study of UMAP and Other Dimensionality Reduction Methods
Supervised UMAP works well for classification but shows clear limitations in incorporating response information for regression tasks.