SaiVLA-JEPA is a family of vision‑language‑action policies that condition on frozen V‑JEPA features and use lightweight auxiliary heads to predict future latent representations during training. Experiments on 10,030 LIBERO‑Plus episodes show that JEPA conditioning improves success rates to about 82.5% versus 71.6% without JEPA, but adding future supervision does not yield a detectable aggregate gain. Variants that adapt latent context or selected parameters provide mixed results, suggesting that latent‑prediction objectives should be evaluated via closed‑loop rollouts rather than latent loss alone.
arXiv:2609.37250v1 Announce Type: cross Abstract: World-action models (WAMs) couple future visual-state prediction with action generation. By adapting video generators or image-editing models pretrai...
arXiv:2611.08156v1 Announce Type: cross Abstract: World-action models adapt pretrained video generators, and vision-language-action models build on pretrained vision-language backbones; both tie poli...
arXiv:2610.09940v1 Announce Type: cross Abstract: Joint-embedding predictive architectures (JEPAs) predict masked or future observations in representation space, offering a natural source of predicti...
EmbodiedSkills is a skill-based agent framework that enhances frozen vision‑language‑action (VLA) policies with world action models for consequence‑aware action selection. It defines reusable skills for observation, context encoding, candidate generation, consequence prediction, outcome evaluation, candidate comparison, and action execution, organized into five stages that support both continuous action chunks and pick‑and‑place primitives. The framework improves success rates on MetaWorld MT50, CLIPort, and CALVIN benchmarks by incorporating outcome predictions and lightweight comparators while keeping the core policy and observation encoders frozen.
arXiv:2609.36645v1 Announce Type: cross Abstract: Future prediction is increasingly used to improve vision-language-action (VLA) policies, based on the premise that anticipating scene evolution encou...
arXiv:2610.27851v1 Announce Type: cross Abstract: This paper shows that JEPA-style latent world models (LWMs) can learn across diverse domains and data sources by adopting the language-conditioned in...
arXiv:2609.32193v2 Announce Type: replace Abstract: Vision Language Action (VLA) models condition actions directly on current visual and language context, without an explicit account of how the scene...
The paper introduces RePlanMem-VLA, a selective visual memory system for Vision‑Language‑Action models that uses a policy’s own plan revisions as an online memory‑write signal. By detecting unusually large disagreements between temporally aligned action predictions, the method identifies task‑relevant events and updates a bounded visual memory while suppressing redundant frames. Experiments on MIKASA‑Robo‑VLA tasks and real‑robot replay data show that RePlanMem‑VLA retains essential observations in every episode and achieves high ROC‑AUC scores for event detection.
MT‑WAM enhances the Fast‑WAM framework by adding complementary supervision for future 2‑D point trajectories and visual features while keeping the original training objectives. A lightweight dual‑stream branch and structured attention mask isolate motion‑specific processing, and motion‑stream tokens provide additional dynamics cues to the action expert. During inference, MT‑WAM skips future‑video prediction, using cached video and motion information to achieve higher success rates on LIBERO, LIBERO‑Plus, RoboTwin 2.0 Clean2Rand, and several real‑world tasks.
arXiv:2608. 11605v1 Announce Type: new Abstract: World Action Models (WAMs) couple future visual prediction with robot action generation, enabling policies to model how the physical world evolves during interaction.
arXiv:2610.28122v1 Announce Type: cross Abstract: Cascade world-action models (WAMs) use predicted visual futures to condition robot actions, but inherit costly iterative inference from diffusion-pre...
The paper introduces DRILL, a bilevel training framework that optimizes world models for policy improvement rather than pixel accuracy. By reweighting prediction errors with policy visitation and confidence (DRILL‑VWI) or using a full meta‑gradient approach (DRILL‑IMG), the authors demonstrate significant gains in downstream policy success rates across multiple manipulation simulators. The work shows that world models can be more effectively trained using the policy gradients they generate, rather than relying solely on pixel‑level prediction metrics.