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Writing study: XR-1: Towards Versatile Vision-Language-Action Models via Learning Unified Vision-Motion Representations

← ICML 2026 oral writing corpus

Shichao Fan, Kun Wu, Zhengping Che, Xinhua Wang, Di Wu, Fei Liao, Ning Liu, Yixue Zhang, Zhen Zhao, Zhiyuan Xu, Meng Li, Qingjie Liu, Shanghang Zhang, Min Wan, Jian Tang · Official ICML 2026 oral record

Figure, table and section ledger

Editorial contribution type: Architecture & representation (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
45 physical pages
Before references / appendix
≈ 9.31 page-equivalents
Main figures / tables
7 / 4
Attached appendix figures / tables
11 / 15
Attached appendix span
≈ 29.91 page-equivalents
Before references / appendix: ≈ 20.69%References / intervening material: ≈ 12.84%Attached appendix: ≈ 66.47%Estimated layout envelopes, not text/artwork area. Unresolved appendix boundaries remain within the undivided post-body material.
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≈ 0.92 pp.2.04%
2. Related Work3≈ 1.11 pp.2.47%
3. Methodology4≈ 2.09 pp.4.64%
4. Experiments6≈ 3.49 pp.7.76%
5. Conclusion9≈ 0.24 pp.0.53%
Acknowledgments10≈ 0.21 pp.0.47%
Impact Statement10≈ 0.05 pp.0.11%
References10≈ 5.78 pp.12.84%
A. Open-Source Resources16≈ 0.11 pp.0.24%
B. Implementation Details16≈ 2.6 pp.5.78%
C. Dataset Curation18≈ 2.29 pp.5.09%
D. Additional Real-World Experiments21≈ 2.3 pp.5.11%
E. Additional Ablation Study23≈ 1.12 pp.2.49%
F. Additional Generalization Analysis24≈ 0.98 pp.2.18%
G. Simulation Results on CALVIN25≈ 0.22 pp.0.49%
H. Visualization of Unified Vision-Motion Codes25≈ 3.91 pp.8.69%
I. Failure Case Analysis on Baseline Methods29≈ 0.93 pp.2.07%
J. Failure Case Analysis on XR-130≈ 0.11 pp.0.24%
K. Representative Tasks30≈ 15.35 pp.34.11%
All detected figure and table locations

Argument and organization

  1. 1 Introduction; 2 Related Work

    Motivates a joint visual-dynamics and motor representation rather than either unimodal latent.

  2. 3 Methodology

    Organizes the representation learner, generalist pretraining and deployment refinement as three stages.

  3. 4 Experiments; 5 Conclusion

    Separates robot comparisons, component ablations, shifted scenarios, out-of-box use and few-shot adaptation.

Problem, claim and contribution

Two stated bottlenecks map to a shared code space, but the claim of causal semantics is stronger than the reconstruction and alignment objectives alone establish.

Introduction and Related Work, pp.2-3; objectives, p.5 · Main paper

Argument and section order

Each training stage specifies its data and loss, including a reduced visual-only objective for human videos without action labels.

Sections 3.2-3.4, pp.4-5 · Main paper

Figures, captions and visual explanation

The overview separates the three training stages and their data sources; result bars distinguish out-of-box and adapted variants and isolate averages from individual tasks.

Figure 2, p.4; Figures 6-7, p.9 · Main paper

Tables, comparisons and evidence

Out-of-box means omitting Stage 3, not an unseen pretraining embodiment; novel-embodiment adaptation uses demonstrations, and the caption discloses joint versus per-task baseline training.

Section 4.4, p.8; Figure 7 caption, p.9 · Main paper

Appendix and reproducibility

The supplement exposes initialization and implementation choices, codebook and modality ablations, and specific precision/coordination failures.

Appendix B, p.17; Appendix E, p.23; Appendix J, p.30 · Main paper

Clarity and writing conventions

Phrases such as monotonic scaling law and embodiment-agnostic semantics should remain tied to the finite tested data fractions and transfer protocols.

Sections 4.3-4.5, pp.8-9 · Main paper

What to reuse—and what not to infer

Transferable lesson: Label representation pretraining, policy pretraining and task adaptation separately so transfer claims reveal exactly which data were available.

Boundary: Unseen during pretraining is not zero-shot deployment; sampled rollout scores and visualization clusters do not establish universal transfer or causal latent semantics.

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 of the main argument through physical p.9, with focused physical-page text inspection on 1, 3, 4, 5, 6, 7, 8, 9. Framing, section progression, mechanism/proof explanation, displays, comparison conditions and conclusions were inspected. This is not exhaustive technical reading, proof verification or reproduction.
Appendix / supplement
Appendix organization and main-text pointers were checked; focused text inspection of physical pages 17, 23, 30. Remaining appendix proofs and details were not exhaustively read. No separately linked supplement inspected.
Visually inspected PDF pages
4, 9

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.