SOURCE-LINKED INTELLIGENCE
TEMPO: Learning Temporal Context for Dynamic Robot Manipulation
Vision-language-action (VLA) models have achieved impressive performance in quasi-static manipulation, but struggle in dynamic manipulation tasks because they operate on a single observation at inference time. We identify two representational failures that underlie this limitation. The first is motion ambiguity, where a single observation does not include scene dynamics and therefore cannot anticipate the future state of moving objects. The second is state aliasing, where visually similar observations from different points in a task require different actions. We argue that these failures persi
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-15T08:56:52.000Z
First collected: 2026-09-20T09:01:24.920Z. This is not the publication date.