LIBERO-Plus

Headlines · 4

1. 3d
   OneWM: unified causal training shows world and action modeling benefit each other

   An arXiv paper posted on Oct 15, 2026 introduces OneWM, a framework for unified causal training that treats actions both as conditions for world prediction and as targets for policy learning inside a shared video-action architecture, in order to test whether world and action modeling help each other. The authors evaluate policy learning and world modeling on three embodied testbeds — LIBERO, LIBERO-Plus and RoboCasa365 — plus real-world policy experiments. According to the abstract, learning the visual consequences of specified actions improves policy performance and generalization across changing scenes, while action learning improves multi-view world-prediction fidelity in the same architecture, leading the authors to answer both research questions affirmatively.

   Hugging Face · Papers · 🔥 0
2. 5d
   WAVE-0 bridges VLA policies and world action models with latent future prediction

   An arXiv paper introduces WAVE-0 (World-model-Augmented VLA Execution), which uses a lightweight 257M-parameter latent world model to connect a pretrained VLA's semantic understanding with explicit future prediction, so anticipated action consequences directly condition the action expert and unify latent planning, future prediction and action generation. The world model is trained directly on ground-truth robot actions with a counterfactual ranking objective, requiring neither pixel reconstruction nor large-scale video pretraining. The authors report success rates of 91.47% on RoboTwin 2.0 and 98.7% on LIBERO, plus 88.0%, 61.4% and 75.0% on LIBERO-Plus, LIBERO-Pro and LIBERO-Para, and a real-world improvement of π0.5 from 38.3% to 73.3%.

   Hugging Face · Papers · 🔥 0
3. 5d
   GreenWAM: a robot world model that also acts as policy

   GreenWAM is a robot world foundation model presented with the recipe that produces it: a shared anatomical action space describing 20 robot families, from bimanual arms to dexterous humanoids, in common joint coordinates; joint training across those families in visual, action-conditioned and world-action modes; and policies that split an action expert off the generator. As a world model it reports top-tier trajectory, depth and 3D accuracy on WorldArena 1.0, ahead of every world-action model and video generator in the comparison, and as a policy it scores among the top entries of the RoboTwin 2.0 leaderboard, reaches the highest zero-shot LIBERO-plus success among the compared methods and is competitive with dedicated vision-language-action and world-action policies on CALVIN and LIBERO. The authors also adapt GreenWAM to a real humanoid, where it succeeds more often than policies adapted from π0.5 and Cosmos 3 Edge, and distill it into a single-step policy for real-time control.

   Hugging Face · Papers · 🔥 0
4. 5d
   RegisterWAM adds learnable registers to world action models for robot control

   An arXiv paper posted on 13 Oct 2026, "Acting Through Registers" by Yiwei Zhang and nine co-authors, examines how World Action Models (WAMs) organize action-relevant information. The authors identify persistent high-norm spatial outliers in WAM representations whose action relevance survives normalization and K/V projection, and propose RegisterWAM, which adds learnable non-spatial registers to the world model and uses them directly for action conditioning. Registers alone do not remove the spatial outliers, but pairing them with observation flow-matching (FM) loss supervision concentrates high-norm responses in the registers; the authors report 81.64% success on LIBERO-Plus without extra embodied pretraining data or foundation models, 93.0% average success on RoboTwin 2.0, and improved real-world manipulation.

   Hugging Face · Papers · 🔥 0

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