Writing study: Rodrigues Network for Learning Robot Actions

ICLR overviewCorpus atlasBy contribution typeVisual galleryRules & templateWriting patternsPaper studies

← ICLR 2026 oral writing corpus

Jialiang Zhang, Haoran Geng, Yang You, Congyue Deng, Pieter Abbeel, Jitendra Malik, Leonidas Guibas · Official ICLR 2026 oral record

Observed writing choices, not acceptance rules. This study examines the inspected manuscript version. It does not establish why the paper was accepted or that its presentation choices should be copied unchanged.

Figure, table and section ledger

Editorial contribution type: Architecture & representation design (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

23 physical pages

Before references / appendix

≈ 9.43 page-equivalents

Main figures / tables

5 / 3

Attached appendix figures / tables

1 / 9

Attached appendix span

≈ 9.89 page-equivalents

Before references / appendix: ≈ 41%References / intervening material: ≈ 16%Attached appendix: ≈ 43%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 heading	Starts on PDF page	Estimated span	Share of entire PDF
1 INTRODUCTION	1	≈ 0.96 pp.	4.17%
2 RELATED WORK: ARTICULATION-AWARE ROBOT LEARNING	2	≈ 0.58 pp.	2.52%
3 NEURAL RODRIGUES OPERATOR	3	≈ 2.01 pp.	8.74%
4 RODRIGUES NETWORK	5	≈ 1.13 pp.	4.91%
5 EXPERIMENTS	6	≈ 3.3 pp.	14.35%
6 CONCLUSIONS AND DISCUSSIONS	9	≈ 0.49 pp.	2.13%
7 ACKNOWLEDGEMENTS	10	≈ 0.16 pp.	0.7%
REFERENCES	10	≈ 3.67 pp.	15.96%
Supplementary Material	14	≈ 0.23 pp.	1%
A RODRIGUES NETWORK FOR ANIMATED CHARACTERS	14	≈ 0.95 pp.	4.13%
B ADDITIONAL DETAILS ON NETWORK ARCHITECTURE	15	≈ 0.33 pp.	1.43%
C EXPERIMENT SETTINGS	15	≈ 2.59 pp.	11.26%
D METHOD AND IMPLEMENTATION DETAILS	18	≈ 3.06 pp.	13.3%
E ADDITIONAL RESULTS	21	≈ 1.83 pp.	7.96%
F ACCELERATING THE MULTI-CHANNEL RODRIGUES OPERATOR WITH	23	≈ 0.89 pp.	3.87%

All detected figure and table locations

Main text

• Figure 1 · p. 1
• Figure 2 · p. 5
• Figure 3 · p. 7
• Figure 4 · p. 7
• Table 1 · p. 8
• Figure 5 · p. 8
• Table 2 · p. 9
• Table 3 · p. 9

Attached appendix

• Table 4 · p. 16
• Table 5 · p. 16
• Table 7 · p. 17
• Table 6 · p. 17
• Table 8 · p. 18
• Table 9 · p. 19
• Table 10 · p. 20
• Table 11 · p. 21
• Figure 6 · p. 22
• Table 12 · p. 22

Argument and organization

1. Classical formula to neural operator

   Makes the inductive bias derivation explicit.

2. Operator to complete network

   Adds local joint/link updates and global information exchange.

3. Toy tests to downstream tasks

   Separates fitting the motivating function from utility in imitation and vision.

Problem, claim and contribution

The introduction uses the analogy between hand-designed filters and CNNs to motivate learning the coefficients of Rodrigues' formula. It immediately names the missing kinematic structure, making the classical-to-neural transition concrete.

§1 and Figure1, pp.1–2 · Main paper

Argument and section order

Equations progress from exact forward kinematics to scalar learnable weights, multiple channels and bidirectional matrix multiplication. The paper states the exact special case without claiming unconstrained learned features remain physical rotations.

§3, Eqs.2–8, pp.3–4 · Main paper

Figures, captions and visual explanation

Figure2 keeps link features, joint features and the global token visually distinct across three sublayers. Its arrows expose which state each component updates, making the block diagram directly traceable to the equations.

Figure2 and §4, pp.5–6 · Main paper

Tables, comparisons and evidence

The experimental sequence explicitly admits that fitting known forward kinematics has limited practical value before moving to simulated manipulation and RGB hand reconstruction. Table2 also shows a PlugCharger loss versus UNet, so the average gain is not universal.

§§5.1–5.3 and Tables1–3, pp.7–9 · Main paper

Appendix and reproducibility

Ablations acknowledge that removing self-attention slightly improves motion-prediction test error. Hardware timings reveal that the full motion network is slower to train than the Transformer despite matched parameter counts, separating parameter efficiency from runtime efficiency.

Tables10–11, pp.20–21 · Main paper

Clarity and writing conventions

The limitation paragraph names missing link geometry and translational joints. Reproduction still requires resolving the 10,000-step prose versus 100,000-step tables and preserving that the hand decoder parameter comparison excludes the shared ViT encoder.

§6, p.10; §§C.1–C.2, pp.15–16; §§5.3 andD.4, pp.9,20 · Main paper

What to reuse—and what not to infer

Transferable lesson: Derive a structured operator step by step, then test both its motivating synthetic function and a downstream application; show exactly which feature types every layer changes.

Boundary: Robot evidence is simulated, with state rather than RGB observations. Hand reconstruction is supervised and domain-specific, not KD or pathology evidence. Learned matrix features are not guaranteed valid rigid transforms; the architecture assumes a known articulated topology.

Apply this lesson using the argument and evidence worksheet.

Inspection record and sources

Checked

2026-09-19

Reading status

Full text + selected visuals inspected

Main text

pp.1–10, §§1–7; references pp.10–13

Appendix / supplement

pp.14–23, A–F: quaternion adaptation, architecture, protocols, baselines, ablations and CUDA

Visually inspected PDF pages

5, 9

• official ICLR 2026 proceedings PDF · 23 pages