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Headlines · 9
- 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 - VLA-Δ supervises physical state transitions in vision-language-action models
The paper proposes VLA-Δ, a framework that treats the physical change between current and future states as an explicit learning target, rather than having vision-language-action (VLA) models map observations directly to demonstrated actions — an approach the authors say is prone to overfitting observation-action correspondences. To support this, the team built a large-scale pretraining dataset and derived physical state-transition annotations covering task-relevant semantics and visual motion changes. At inference the model still generates actions directly from current observations with no added overhead; the authors report performance competitive with the state of the art on LIBERO and RoboTwin and better than π0.5 in real-robot experiments, with code released.
Hugging Face · Papers · 🔥 0 - Temporal Flow Policy adds physical time to robot policy memory
An arXiv robotics paper submitted on 14 Oct 2026 introduces Temporal Flow Policy (TFP), treating physical time as a missing dimension of robot policy memory. The authors argue that existing policy memories evolve with observation sequences, implicitly treating every update as the same amount of physical time, even though identical observation histories can span different durations and require different actions. TFP learns memory dynamics that retain past events and advance the memory state over measured time intervals, conditioning diffusion and flow-matching policies through that state and training with their native imitation objectives. In controlled comparisons with Diffusion Policy and π0.5, the authors report that TFP achieves higher average success than recurrent and state-space memories given the same intervals, and than a variant that feeds the interval to a learned gate; they also report gains on real-robot timing/memory tasks and LIBERO evaluations.
Hugging Face · Papers · 🔥 0 - G^3VLA adds calibrated camera geometry to pretrained VLA models
An arXiv paper proposes G^3VLA, a camera-aware geometric module that injects calibrated camera geometry into the visual-token stream of a pretrained VLA model without altering its action space or original action-learning objective. It combines intrinsic-conditioned ray embeddings, projective positional encoding (PRoPE) and bidirectional cross-view fusion, with geometric supervision from ground-truth point maps or confidence-gated π³X teacher predictions, so no depth sensors or manual geometric annotations are needed. The authors report consistent gains on LIBERO and RoboCasa24 with π0, improved five-task RoboTwin2.0 averages with three cameras under ground-truth supervision, and better robustness to unseen camera viewpoints on camera-shifted LIBERO-Goal and real robots.
Hugging Face · Papers · 🔥 0 - 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 - 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 - TacitVLA hides reasoning from the action expert, cutting VLA latency up to 3.4x
A new paper introduces TacitVLA, a reasoning vision-language-action (VLA) model whose action expert never attends to the reasoning text generated during the same call, so actions do not have to wait for autoregressive decoding. The authors argue the reasoning supervision already improves the backbone representation, and across four benchmarks spanning manipulation and autonomous driving (RoboMME, RMBench, LIBERO and PhysicalAI-AV) they report matching an explicit-reasoning counterpart at up to 3.4x lower latency while matching the latency of the same architecture trained without reasoning at up to +17.1% success. In absolute terms the paper reports 82.7% on RoboMME Counting (vs. 66.8% prior best), 77.5% on RMBench (vs. 56.5%), 72.9% on LIBERO-Plus and a 52.5% scene score on the public split of the PhysicalAI-AV closed-loop driving challenge. All figures are self-reported and not yet independently reproduced.
Hugging Face · Papers · 🔥 0 - Stanford's Fei-Fei Li team open-sources OpenWAM world-action model framework
A Stanford team including Fei-Fei Li, Jiajun Wu and Ehsan Adeli published OpenWAM, an open framework for composable world-action models that aims to disentangle the many ways video prediction and action generation can be coupled in this emerging class of robot policies. OpenWAM starts from Alibaba's open Wan2.2-5B video model, continues pretraining on roughly 3.34 million robot-and-human interaction videos (nominally about 14,600 hours) for 14 days on 32 B200 GPUs, then joins a 5B video expert with a 2B action expert via a shared Mixture-of-Transformers architecture with four configurable interaction orders (VTA, ATV, Joint, Decoupled). The authors report 98.6% average success for VTA across the four LIBERO subsets and 92.1% / 91.9% average success for VTA and Joint on three real dual-arm Franka FR3 tasks, plus a LIBERO-Long-CF counterfactual dataset; training, evaluation and composition code and pretrained video weights are open-sourced.
36氪 人工智能 · 🔥 0 - Zhengxing Innovation launches 24/7 Physical AI retail solution with H1 and C1 robots
At the 22nd Asia Pacific Retailers Convention and Exhibition (APRCE 2026), Zhengxing Innovation unveiled a Physical AI solution for open retail settings built around human-robot collaboration: the general-purpose bipedal humanoid H1, the wheeled-arm handling and shelf-restocking robot C1 designed for narrow aisles, and M1, a unified robot management platform handling task allocation, status monitoring and human takeover. The company says its in-house lightweight world-action model SLM-0.5 reaches 98.6% average task success on the LIBERO benchmark with 24x faster inference than mainstream approaches, and that its RLinf-based reinforcement-learning post-training system STEAM achieves 95% success on a designated-product restocking task. Commercial service is planned for 2027 via direct purchase or RaaS subscription, with joint testing alongside global retail chains.
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