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...
By Adam Lee, Shobhit Agarwal, June Pyo Suh
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.
By Zuojin Tang, Feifan Luo, Haoyun Liu, Botai Yuan, Dekang Qi, Ronghan Chen, Yandan Yang, Tong Lin, Xinyuan Chang, Mu Xu, Bin Liu, De Ma, Zhiheng Ma
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.
By Jiawei Fan, Sifeng Wang, Yuqing Hou, Anbang Yao
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.
By Tuo An, Chuhao Zhou, Jiahua Dong, Jingliang Li, Jiaqi Bai, Jindou Jia, Jianfei Yang
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.
By Peize Li, Ruimeng Zhang, Ru Zhang, Cong Huang, Kai Chen, Shanghang Zhang