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
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 heading | Starts on PDF page | Estimated span | Share of entire PDF |
|---|---|---|---|
| 1. Introduction | 2 | ≈ 0.82 pp. | 6.83% |
| 2. Related Work | 2 | ≈ 0.82 pp. | 6.83% |
| 3. Methodology | 3 | ≈ 1.21 pp. | 10.08% |
| 4. Benchmarks and Datasets | 4 | ≈ 0.91 pp. | 7.58% |
| 5. Experiments | 5 | ≈ 3.04 pp. | 25.33% |
| 6. Conclusion and Discussion | 8 | ≈ 0.2 pp. | 1.67% |
| Acknowledgement | 9 | ≈ 0.04 pp. | 0.33% |
| References | 9 | ≈ 3.91 pp. | 32.58% |
All detected figure and table locations
Main text
Attached appendix
No recognized caption labels.
Argument and organization
- 1 Introduction; 2 Related Work
Asks whether motion-focused video tokens can transfer without action annotations during pretraining.
- 3 Methodology; 4 Benchmarks and Datasets
Separates tokenizer learning, instruction-conditioned motion pretraining and action-supervised co-fine-tuning.
- 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.