RoboTwin 2.0

Headlines · 4

1. 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
2. 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
3. 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
4. Oct 6
   Long-WAM scales world-action model context for real-time robot control

   Long-WAM is a model-system framework for scaling the context of causal world-action models under real-time robot-control constraints; its authors report that longer histories only pay off when the video foundation is pretrained autoregressively. On RoboCasa GR-1, raising context from 0.0 to 19.2 seconds lifted success from 63.3% to 78.7%, while a bidirectionally pretrained initialization showed no net gain, and robot-domain autoregressive pretraining further raised peak success on GR-1 and LIBERO-Long. The paper also reports deployment on RTX 5090, DGX Spark and Jetson AGX Thor, with each action chunk including future-video latent prediction taking 107.4 ms on RTX 5090, plus real-time long-horizon manipulation on Unitree G1 and YAM reaching 95% success on dynamic cup stacking, where it says Pi0.5 and Fast-WAM succeeded in none of 20 trials.

   Hugging Face · Papers · 🔥 0

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