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Writing study: Moto: Latent Motion Token as the Bridging Language for Learning Robot Manipulation from Videos

← ICCV 2025 oral writing corpus

Yi Chen, Yuying Ge, Weiliang Tang, Yizhuo Li, Yixiao Ge, Mingyu Ding, Ying Shan, Xihui Liu · Official ICCV 2025 oral record

Figure, table and section ledger

Editorial contribution type: Training, objectives & optimization (primary).

Layout extraction, not a required paper template. Counts are distinct caption labels; a multi-panel figure counts once. Section spans are estimated from heading positions, including floats and whitespace. Method and limitations.

Canonical PDF
12 physical pages
Before references / appendix
≈ 8.09 page-equivalents
Main figures / tables
12 / 3
Attached appendix figures / tables
0 / 0
Attached appendix span
No boundary detected; not assumed zero
Before references / appendix: ≈ 67.42%References / intervening material: ≈ 32.58%Estimated layout envelopes, not text/artwork area. Unresolved appendix boundaries remain within the undivided post-body material.

Extraction notes: No attached appendix boundary detected. Zero means no recognized caption, not independently verified absence.

Section-by-section lengths and source pages

The title/abstract/front matter precede the first numbered heading. A span ends at the next detected top-level heading, not at the last sentence; end-page whitespace is included. References and statements are listed separately. These are not measurements of text-only or artwork area.

Top-level headingStarts on PDF pageEstimated spanShare of entire PDF
1. Introduction2≈ 0.82 pp.6.83%
2. Related Work2≈ 0.82 pp.6.83%
3. Methodology3≈ 1.21 pp.10.08%
4. Benchmarks and Datasets4≈ 0.91 pp.7.58%
5. Experiments5≈ 3.04 pp.25.33%
6. Conclusion and Discussion8≈ 0.2 pp.1.67%
Acknowledgement9≈ 0.04 pp.0.33%
References9≈ 3.91 pp.32.58%
All detected figure and table locations

Attached appendix

    No recognized caption labels.

    Argument and organization

    1. 1 Introduction; 2 Related Work

      Asks whether motion-focused video tokens can transfer without action annotations during pretraining.

    2. 3 Methodology; 4 Benchmarks and Datasets

      Separates tokenizer learning, instruction-conditioned motion pretraining and action-supervised co-fine-tuning.

    3. 5 Experiments; 6 Conclusion and Discussion

      Answers interpretability, learned-prior and control-performance questions, then studies data efficiency and motion-loss retention.

    Problem, claim and contribution

    A bridging motion language is positioned between video observation and embodiment-specific control, with the remaining action-adaptation gap stated explicitly.

    Sections 1 and 3.4, physical pp.2,4 · Main paper

    Argument and section order

    Three experimental questions mirror tokenizer semantics, pretraining priors and downstream policy utility rather than collapsing them into one benchmark score.

    Section 5, p.5 · Main paper

    Figures, captions and visual explanation

    The three-stage overview marks frozen encoders/tokenizer, inserted action queries and the retained motion-token objective, making supervision transitions visible.

    Figure 2, p.3 · Main paper

    Tables, comparisons and evidence

    CALVIN results expose each method’s observation space and define chain length over 1,000 trials; SIMPLER retains task-specific non-wins despite the best aggregate.

    Tables 2–3, p.7 · Main paper

    Appendix and reproducibility

    Implementation, benchmark settings, baseline details and additional generated trajectories are routed to supplementary material, not inspected here.

    Sections 3.1,4,5.2–5.3, pp.3–4,6–7 · Main paper

    Clarity and writing conventions

    The human-video extension is called a preliminary study and separately reports the added 105k SSV2 videos alongside robot-video pretraining.

    Section 5.3, pp.7–8 · Main paper

    What to reuse—and what not to infer

    Transferable lesson: Make every change in supervision and observation privileges visible across pretraining, fine-tuning and evaluation.

    Boundary: Action-free pretraining is not action-free learning: downstream policy training uses labeled expert trajectories. Token visual consistency and trajectory likelihood are not proofs of causal motion understanding or calibrated rewards; small real-world task trials and selected human-video transfer do not establish general-purpose safe control.

    Apply this lesson using the argument and evidence worksheet.

    Inspection record and sources

    Checked
    2026-10-07
    Reading status
    Writing-focused manuscript scan + selected visuals inspected
    Main text
    Writing-oriented scan across the main manuscript, physical PDF pp.1–8, covering framing, section progression, method explanations, captions, comparison protocols and concluding scope; reference boundary checked. Selected full-page rasters were inspected. Not exhaustive line-by-line reading, proof verification or experimental reproduction.
    Appendix / supplement
    Main-paper appendix/supplement references were checked. A separate supplementary source is linked in the inventory but was not retrieved or inspected; its contents are not included in the reading or visual-page counts.
    Visually inspected PDF pages
    3, 7

    This is a writing-focused scan of the manuscript and the stated supplementary scope: framing, organization, visual explanation, evidence presentation and wording. It is not a line-by-line proof audit, reproduction of results or exhaustive scientific review.