SOURCE-LINKED INTELLIGENCE
TemporalFlow-VLA: Learning Physically Grounded Execution History for Long-Horizon Robot Manipulation
Vision-language-action (VLA) models leverage pretrained vision-language representations for robot control, yet simply adding historical frames does not reliably capture recent physical change. This is especially problematic in multi-stage manipulation, where visually similar states may require different actions depending on prior execution. To address this challenge, we present TemporalFlow-VLA, which learns compact execution history through physically grounded temporal supervision. Using recorded robot states, robot geometry, and calibrated cameras, we construct robot-surface temporal flow as
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-08-27T09:00:56.000Z
First collected: 2026-09-21T08:51:59.673Z. This is not the publication date.