The paper introduces SPT-Flow, a continuous‑time world model that samples a single latent per trajectory and aligns a free‑running ODE rollout with demonstration states at multiple readout times. Experiments on LIBERO show that SPT-Flow improves best‑of‑10 PSNR by 2.0–2.4 dB at mid‑trajectory and final frames and reduces mean LPIPS by 23 % compared to a retrained ODEWorld baseline, while maintaining comparable inference cost. The authors attribute these gains to better utilization of trajectory‑associated latent information and demonstrate that learned latent conditioning adds diversity and best‑of‑K performance. whyItMatters":"SPT-Flow demonstrates that careful alignment of ODE rollouts with demonstration states can significantly enhance reconstruction quality and diversity in continuous‑time world models, offering a practical improvement over existing baselines."
arXiv:2610.26903v1 Announce Type: new Abstract: Action-conditioned world models support planning and policy evaluation by predicting future states. However, learning an accurate one-step predictor ca...
arXiv:2607. 27138v1 Announce Type: cross Abstract: Vision-language-action (VLA) models remain constrained by scarce action-labeled robot data, whereas action-free videos offer abundant observations of physical change.
The paper introduces Kinematic MeanFlow (K-MF), a one‑step action generation policy for Robotic Foundation Models that addresses instability in the MeanFlow framework. By decoupling the time derivative into two sub‑interval terms, K-MF captures early and late denoising dynamics separately, reducing error amplification. Experiments show K-MF achieves faster inference—reducing action‑head latency by 67.5%–74.4% and overall end‑to‑end latency by 30.3%–54.9%—while outperforming multi‑step flow matching on various tasks.
Flow-based policies for language-conditioned multi-task manipulation commonly share a single $\textit{Gaussian}$ source prior and a common linear transport path across tasks. However, reliably executi...
TOPS (Task‑Oriented Priors and Schedules) adapts flow‑based policies for language‑conditioned multi‑task robot manipulation by customizing the flow source prior and interpolation schedule per task without changing the network architecture. It constructs multimodal task sources from reverse‑mapped expert action sequences, allowing language instructions to select the generation start point, and adjusts the denoising schedule based on source‑target covariance mismatch to reduce velocity‑prediction risk. Experiments on LIBERO, LIBERO‑Plus, CALVIN, and real‑world tasks show significant performance gains, including a jump from 53.44 % to 69.67 % success on LIBERO‑10 with a single integration step and a five‑fold reduction in inference time.
arXiv:2607. 29235v1 Announce Type: cross Abstract: Although world-action models (WAMs) enhance long-horizon robot control by predicting visual evolution before acting, long-horizon reliability demands repeated re-grounding in real observations--not recursive rollout.
In this paper, we study how to achieve one-step action generation in Robotic Foundation Models (RFMs), aiming to overcome the high inference latency of multi-step flow matching. MeanFlow provides a pr...
The paper introduces the Dual-Latent World Model (Dual-WM), which separates local execution and long-range planning into distinct latent spaces and dynamics models. A new learning method, Long-Horizon Representation Learning with Weighted Rollout (LoRe), supervises predictions at both levels using exponential horizon weights. Experiments on five goal-conditioned visual control tasks show that Dual-WM improves success rates over strong baselines, especially at longer horizons.
The paper challenges the common practice of selecting latent world models based on one‑step prediction loss, arguing that this metric poorly predicts planning performance. Using a frozen‑decoder protocol, the authors decompose one‑step error into encoder‑inherent and predictor‑added components and evaluate goal‑conditioned planning across three continuous‑control environments. Their experiments show that improvements in one‑step fidelity do not correlate with planning success, with correlation coefficients near zero and inconsistent exchange rates across tasks.
arXiv:2606. 08657v1 Announce Type: cross Abstract: Diffusion-based visuomotor policies operating directly in raw action spaces conflate scene comprehension with trajectory generation within a single denoising process.
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...
arXiv:2607. 04978v1 Announce Type: new Abstract: Joint-Embedding Predictive Architectures (JEPAs) underpin a growing family of latent world models for control from raw pixels, but every existing JEPA world model commits at training time to a single inference paradigm: either trajectory optimisation in a learned dynamics model, or direct behaviour cloning.