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.
RoboCasa: Large-scale simulation of everyday tasks for generalist robots
2 Pith papers cite this work. Polarity classification is still indexing.
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PhysBrain 1.0 extracts scene elements, spatial dynamics, actions and depth relations from human egocentric video to create QA supervision for VLMs, then transfers the resulting physical priors to VLA policies via capability-preserving adaptation.
citing papers explorer
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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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PhysBrain 1.0 Technical Report
PhysBrain 1.0 extracts scene elements, spatial dynamics, actions and depth relations from human egocentric video to create QA supervision for VLMs, then transfers the resulting physical priors to VLA policies via capability-preserving adaptation.