MA-BC partitions divergent expert data and pools non-conflicting pairs to achieve faster convergence to Pareto-optimal policies in MOMDPs, with a matching minimax lower bound.
Bridgedata v2: A dataset for robot learning at scale
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ForgeVLA enables federated VLA model training from unlabeled vision-action pairs by recovering language via embodied classifiers and using contrastive planning plus adaptive aggregation to avoid feature collapse.
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Split the Differences, Pool the Rest: Provably Efficient Multi-Objective Imitation
MA-BC partitions divergent expert data and pools non-conflicting pairs to achieve faster convergence to Pareto-optimal policies in MOMDPs, with a matching minimax lower bound.
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ForgeVLA: Federated Vision-Language-Action Learning without Language Annotations
ForgeVLA enables federated VLA model training from unlabeled vision-action pairs by recovering language via embodied classifiers and using contrastive planning plus adaptive aggregation to avoid feature collapse.