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cs.RO2026
Flex-: A Multi-Stream World-Action Model with Compute Flexibility
Ge Yan, Jinghao Liu, Yuzhi Fan +4
World-action models (WAMs) predict the future to act better, but nearly all of them predict only RGB latents, trained purely for pixel reconstruction, with no explicit signal for t…
cs.RO2026
SeeTraceAct: Visibility-Aware Latent Planning from Cross-Embodiment Demonstration Videos
Jaehyeon Son, Junhyun Kim, Kyle Kam +7
Vision-language-action models (VLAs) are promising general-purpose robot policies, but adapting them to new tasks typically requires costly task-specific teleoperation data. As an…
cs.RO2026
Robometer: Scaling General-Purpose Robotic Reward Models via Trajectory Comparisons
Anthony Liang, Yigit Korkmaz, Jiahui Zhang +14
General-purpose robot reward models are typically trained to predict absolute task progress from expert demonstrations, providing only local, frame-level supervision. While effecti…