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

1. Quick overview of the paper

• Difficulty rating: ★★★★☆. It is necessary to understand the video generation model, inverse dynamics model, VLA strategy, robot control closed loop and experimental evaluation indicators.
• Keywords: Video generation model, robot manipulation, inverse dynamics model, VLA, hierarchical control, dexterous manipulation.
• Core contribution 1: The paper examines a specific question: how far can today's cutting-edge video generation models advance robot operation in a zero-human demonstration setting. This means that the paper does not just propose an architecture, but first experimentally evaluates the upper limit of the "video model + IDM" route.
• Core contribution 2: The paper finds that Veo-3 can generate task-level trajectories that are generally correct and follow instructions, but the low-level contact control accuracy is insufficient. This means that video models are more suitable as high-level motion priors rather than as direct replacements for control strategies.
• Core contribution 3: The paper proposes Veo-Act, which uses the video model as a high-level planner, VLA as a low-level executor, and uses the gate signal of the multi-head IDM to decide when to switch. This means that the system attempts to retain the generalization capabilities of the video model while leveraging the reactive contact control capabilities of the VLA.

2. Motivation

2.1 What problem should be solved?

The paper focuses on generalized robot operations in open environments: robots need to grasp and place in different object geometries, semantic instructions, and distractor scenarios. Existing VLA models rely on large-scale robot data, and action modalities need to be introduced when converting from VLM to VLA; the paper points out that this will make it difficult to completely retain the generalization knowledge of the pre-trained VLM.

The failure scenarios constructed in this paper include: the target object is not within the field of view of the wrist camera, there are interference objects with similar appearance, and non-target objects are located on the path of the robotic arm. These scenarios do not simply examine "whether it can be caught", but examine whether the strategy can still follow the target instructions under visual, semantic and path interference.

2.2 Limitations of existing methods

• VLA route: Models such as RT-2, OpenVLA, $\pi_0$, and $\pi_{0.5}$ map visual language input to actions, but the introduction of action modalities may weaken the retention of VLM pre-training knowledge; the paper uses this to explain the failure of VLA under semantic confusion and distribution changes.
• Video Model + IDM Route: This route maintains the native input and output interface of the video model and can better retain the generalization ability of the video model; however, the generated video is still not accurate enough in contact with rich low-level physical interactions, and IDM will amplify the control error after converting visual errors into actions.
• Contradictions of a single route: VLA is good at low-level closed-loop contact but has limited semantic generalization; video models are good at generating generally correct task-level trajectories but low-level control is unstable. The motivation behind Veo-Act is to divide the work between the two.

2.3 The solution ideas of this article

At the high level, Veo-3 generates a video trajectory to complete the task based on the initial image and language prompt; in the middle, the multi-head IDM converts the inter-frame changes into action clips and predicts the interaction gate; during low-level execution, the system follows the video planning action by default. When the gate indicates that it has entered the contact interaction stage, it switches to the VLA strategy to perform detailed operations, and then switches back to the remaining planning queue.

Figure 1: Comparison of three control pipelines: VLA, Video model + IDM, and Veo-Act.

3. Summary of related work

3.1 Related work of the thesis self-description

3.2 Direct comparison with previous works

4. Detailed explanation of method

4.1 Method overview

The data flow of Veo-Act is: initial observation $I_0$ and task prompt → Veo-3 generates future frame sequence $I^{*}_{0: n}$ → multi-head IDM performs inverse dynamics on adjacent frames, outputs action fragment $a^{*}_{0: n-1}$ and gate signal → smoother obtains $\bar{a}^{*}_{0: n-1}$ → gradually pops from the action queue during execution; if real-time gate $G_t$ If it continues to be higher than the threshold $\tau$, switch to the low-level VLA policy $\pi_{\mathrm{VLA}}$; after the gate is lowered, cut off the interaction interval and restore the planning queue.

Figure 2: Hierarchical planning and control pipeline. The original PDF image has been converted to PNG.

Figure 3: Comparison of three execution trajectories of pure IDM, one-time switching, and complete Veo-Act.

4.2 Method evolution

4.3 Core design and mathematical derivation

Figure 4: Multi-head IDM training process.

Appendix figure: Example of how smoother handles an action dimension.

4.4 Implementation Points (For reproducibility)

Hierarchical Veo-Act inference
1. reset environment and sample task
2. build video-generation prompt from task
3. generate future frames I*_{0:n} with Veo-3
4. run IDM.action over generated frames to get a*_{0:n-1}
5. smooth actions and enqueue them into Q
6. for each real timestep:
   - observe current image/state
   - compute gate G_t = IDM.gate(I_{t-1}, I_t)
   - if gate stays low: execute next planned action from Q
   - if gate stays high: execute low-level VLA action
   - if gate returns low: prune high-gate queue segment and resume plan

5. Experiment

5.1 Experimental setup

training configuration

5.2 Main results

Paper summary: Under the Experimental conditions of the simulation experiment, the baseline overall success is 22/90 = 0.24, and the Veo-Act is 62/90 = 0.69; the summary of all overall successes in simulation + real life, the baseline is 102/228 = 0.45, and the Veo-Act is 182/228 = 0.80.

Simulation success rate: yellow is instruction-following, green is overall.

Real robot success rate.

5.3 Ablation experiment

Ablation results on wrist camera invisible settings.

5.4 Supplementary experiments (from appendix)

• Four categories of detailed settings: The appendix gives qualitative illustrations of invisible object, pass-by, similar-object distractors, and richer semantics, and supplements these contents with experimental setup instructions.
• Pure IDM results: The appendix reports the results of pure IDM under the same protocol for isolating the ability of video prior + IDM itself; pure IDM has been explained as a variant in the main methods section.
• Failure analysis: The appendix lists the types of failures in the experimental setting as a source of information on the limitations and boundaries of §6.
• Action smoother visualization: The appendix shows a single action dimension before and after smoothing, illustrating the actual effect of the smoother.

Appendix setting 1: control and ours for invisible object condition.

Appendix Setting 2: pass-by interaction.

Appendix Setting 3: similar-object distractors.

6. Analysis and Discussion

6.1 Analysis and explanation of the results given in the paper

• The paper explains that the baseline decreases systematically under Experimental conditions, indicating that the wrist camera field of view, similar object interference and pass-by interference do expose the instruction-following failure modes of VLA.
• The paper attributes Veo-Act's improvement to the generally correct task-level trajectories generated by the video model and the closed-loop recovery capabilities provided by level switching during the contact phase.
• The paper points out that multi-head design improves overall success because joint learning of action prediction and interaction detection is more conducive to stage switching and end-to-end execution.

6.2 Limitations of the author's statement

It is clearly pointed out in the text and conclusion of the paper that the current video model cannot accurately complete most low-level contact-rich operations: Veo-3 + IDM can generate generally correct trajectories, but the low-level control accuracy is insufficient, especially in the physical contact stage. The paper also points out that this route is constrained by the fidelity of the underlying video generation model, and the limitations are more obvious in high-dimensional action spaces and multi-point contact dynamics scenarios such as dexterous operation.

The appendix failure analysis is included here: failures mainly correspond to target identification, path interference, contact execution and low-level switching in the confounded setting. Since the paper does not expand these failures into new method suggestions in the main text, this report only objectively records them as applicable boundaries.

6.3 Applicable boundaries and discussions clearly stated in the paper

• Applicable conditions: The video model can generate approximately correct task-level trajectories, the IDM can recover executable actions from inter-frame changes, and the low-level VLA can handle the contact phase.
• Not applicable or risky scenarios: Video predictions were significantly inaccurate in contact dynamics, target/container relationships were generated incorrectly, and gate detection was unstable leading to premature or late switching.
• Future trends: It is concluded that as video generative models continue to improve, video models can become valuable components in generalized robot learning; this is stated as a summary of the paper and not as additional recommendations of this report.

Self-acceptance