Writing study: Learning Human-Robot Collaboration via Heterogeneous-Agent Lyapunov Policy Optimization
← ICML 2026 oral writing corpus
Hao Zhang, Yaru Niu, Yikai Wang, Ding Zhao, Eric H. Tseng · Official ICML 2026 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
- 15 physical pages
- Before references / appendix
- ≈ 8.54 page-equivalents
- Main figures / tables
- 8 / 3
- Attached appendix figures / tables
- 0 / 6
- Attached appendix span
- ≈ 3.91 page-equivalents
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 | 1 | ≈ 1 pp. | 6.67% |
| 2. Related Work | 2 | ≈ 0.51 pp. | 3.4% |
| 3. Preliminaries | 2 | ≈ 0.51 pp. | 3.4% |
| 4. Methodology: The HALO Framework | 3 | ≈ 1.47 pp. | 9.8% |
| 5. Theoretical Analysis | 4 | ≈ 0.57 pp. | 3.8% |
| 6. Experiments and Results | 5 | ≈ 3.41 pp. | 22.73% |
| 7. Conclusion | 8 | ≈ 0.32 pp. | 2.13% |
| Acknowledgements | 9 | ≈ 0.08 pp. | 0.53% |
| Impact Statement | 9 | ≈ 0.32 pp. | 2.13% |
| References | 9 | ≈ 2.54 pp. | 16.93% |
| A. Detailed theoretical derivations | 12 | ≈ 1.54 pp. | 10.27% |
| B. Implementation and experimentation details | 13 | ≈ 2.37 pp. | 15.8% |
All detected figure and table locations
Main text
Argument and organization
- Problem and contribution
Locates the proposed stability certificate in policy-parameter space and defines coordination disagreement as the difference between independent and team gradient fields.
- Argument sequence
A constrained projection and HVP implementation precede theorem conditions, then task performance, mechanism diagnostics and real-world transfer are separated.
- Evidence and verification
The descent statement requires a state-dependent step-size bound and smoothness; the physical SLH table retains a 20% drop rate rather than supporting an absolute safety claim.
Problem, claim and contribution
Locates the proposed stability certificate in policy-parameter space and defines coordination disagreement as the difference between independent and team gradient fields.
Sections 1 and 4, pp. 2-3 · Main paper
Argument and section order
A constrained projection and HVP implementation precede theorem conditions, then task performance, mechanism diagnostics and real-world transfer are separated.
Sections 4-6, pp. 3-8 · Main paper
Figures, captions and visual explanation
Simulation infrastructure and reward dynamics share one figure, while physical demonstrations mark temporal progression and accompany a five-trial table retaining nonzero drop rates.
Figure 2, p. 5; Figure 8 and Table 3, p. 8; both PNGs inspected · Main paper
Tables, comparisons and evidence
The descent statement requires a state-dependent step-size bound and smoothness; the physical SLH table retains a 20% drop rate rather than supporting an absolute safety claim.
Assumption 5.1/Theorem 5.2, pp. 4-5; Table 3, p. 8 · Main paper
Appendix and reproducibility
Baseline documentation gives the same network size and training steps but explicitly different partner representations and collaborative features, making the comparison's residual confounds visible.
Appendix B.4 and Tables 7-9, p. 15 · Main paper
Clarity and writing conventions
Short proof summaries explain the role of the Lyapunov potential, but strongly worded coordination/stability claims must retain the parameter-space and small physical-study boundaries.
Section 5, pp. 4-5; Section 6.5, pp. 7-8 · Main paper
What to reuse—and what not to infer
Transferable lesson: Keep the formal certificate's space and assumptions separate from behavioral outcomes, and report failures alongside physical demonstrations.
Boundary: Gradient-gap convergence is not physical safety or global reward optimality; five trials, motion-capture dependence and changed partner features limit deployment generalization.
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-focused distributed manuscript scan, not exhaustive technical/proof validation. Sections 1-7, pp. 1-8: coordination-gradient mismatch, projected update, HVP implementation, conditional convergence analysis, simulation diagnostics and physical collaboration tests.
- Appendix / supplement
- Writing-focused appendix scan; code, data and external assets not executed. Appendices A-B, pp. 12-15: gradient/projection and convergence derivations, hierarchical controller, hyperparameters and scripted-partner baseline implementation.
- Visually inspected PDF pages
- 5, 8
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.