Veo-Act: How Far Can Frontier Video Models Advance Generalizable Robot Manipulation?

• URL: http://arxiv.org/abs/2604.04502v1
• Date: Mon, 06 Apr 2026 07:57:52 GMT
• Title: Veo-Act: How Far Can Frontier Video Models Advance Generalizable Robot Manipulation?
• Abstract summary: We study how far an advanced video generation model such as Veo-3 can support generalizable robotic manipulation.<n>We develop a hierarchical framework, Veo-Act, which uses Veo-3 as a high-level motion planner and a VLA policy as the low-level executor.
• Score: 10.4712787665679
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Video generation models have advanced rapidly and are beginning to show a strong understanding of physical dynamics. In this paper, we investigate how far an advanced video generation model such as Veo-3 can support generalizable robotic manipulation. We first study a zero-shot approach in which Veo-3 predicts future image sequences from current robot observations, while an inverse dynamics model IDM recovers the corresponding robot actions. The IDM is trained solely on random-play data, requiring neither human supervision nor expert demonstrations. The key intuition is that, if a video model can generate physically plausible future motions in image space, an IDM can translate those visual trajectories into executable robot actions. We evaluate this "Veo-3+IDM" approach in both simulation and the real world using a high-dimensional dexterous hand. We find that, owing to the strong generalization capability of frontier video models, Veo-3+IDM can consistently generate approximately correct task-level trajectories. However, its low-level control accuracy remains insufficient to solve most tasks reliably. Motivated by this observation, we develop a hierarchical framework, Veo-Act, which uses Veo-3 as a high-level motion planner and a VLA policy as the low-level executor, significantly improving the instruction-following performance of a state-of-the-art vision-language-action policy. Overall, our results suggest that, as video generation models continue to improve, video models can be a valuable component for generalizable robot learning.

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