FlexLAM trains variable-length latent actions with nested dropout to match or exceed fixed-capacity LAMs at every token budget under scarce-label supervision without new architectures or losses.
DynaMo: In-Domain Dynamics Pretraining for Visuo-Motor Control , url =
2 Pith papers cite this work. Polarity classification is still indexing.
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Sparse2Act pretrains sparse 3D encoders via masked action-alignment supervision, yielding reusable representations that reach 86.9% success on LIBERO-10 and enable cross-domain transfer.
citing papers explorer
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FlexLAM: Resolving the Bottleneck Trade-off in Latent Action Learning
FlexLAM trains variable-length latent actions with nested dropout to match or exceed fixed-capacity LAMs at every token budget under scarce-label supervision without new architectures or losses.
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Sparse2Act: Learning Action-Aligned Sparse 3D Representations for Cross-Domain Robot Manipulation
Sparse2Act pretrains sparse 3D encoders via masked action-alignment supervision, yielding reusable representations that reach 86.9% success on LIBERO-10 and enable cross-domain transfer.