Scaling robot manipulation policies requires large amounts of diverse, action-labeled data, yet collecting such data in the real world remains expensive and difficult to scale. Existing methods either depend on human teleoperation, rely on simulation with limited visual realism, or use human videos that lack embodiment...
F. Jiang, Shu Chen, Yuan-Fei Wang et al.· 0 citations
AffordanceWAM is introduced, an affordance-aware generative World Action Model that represents object-centric spatiotemporal affordance through Scalar Affordance and Affordance Heatmap, within the generated future World, and supports affordance as an effective interface for both vision-language-action learning and huma...
This work argues that update admission must be assessed through both error control and retained learning opportunities at a stated interaction budget, and identifies a concrete failure: a range-based confidence gate cannot certify unchanged old-task behavior within otherwise substantial budgets.
LD4WAM is presented, which pairs a Latent Dynamics Model trained with semantic reconstruction and real motion alignment with a World Dynamics Action Model built as a mixture-of-transformers (MoT), which preserves full future-video generation and uses learnable queries to distill these latent dynamics from generated fut...
Zhen Shen, Jia-Qi Liang, Jasper Lu et al.· 3 citations
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