| --- |
| datasets: |
| - avihaig/dlogps-cables |
| license: cc-by-4.0 |
| pipeline_tag: robotics |
| tags: |
| - pytorch |
| - graph-transformer |
| - graphgps |
| - attention-bias |
| - learned-simulator |
| - physics-simulation |
| - deformable-linear-objects |
| - cable |
| - robotics |
| --- |
| |
| # Predicting Cable Dynamics with Physical Attention Bias: checkpoints |
|
|
| The final checkpoints (step 100,000) of the 39 runs reported in **Predicting |
| Cable Dynamics with Physical Attention Bias**, an extended abstract at the |
| NeurIPS 2026 Workshop on Symmetry and Geometry in Neural Representations |
| (NeurReps). |
|
|
| **Authors:** Avihai Giuili\*, Rotem Atari\*, Avishai Sintov (Tel Aviv |
| University), Maya Bechler-Speicher (Meta AI). \*Equal contribution. |
| |
| **Paper:** [arXiv:2610.11975](https://arxiv.org/abs/2610.11975) · |
| **Code:** [github.com/avihaig/dlogps](https://github.com/avihaig/dlogps) · |
| **Dataset:** [avihaig/dlogps-cables](https://huggingface.co/datasets/avihaig/dlogps-cables) |
| |
| ## The model |
| |
| A GraphGPS-style learned simulator of a falling cable (33 vertices). Each layer |
| runs a GatedGCN over the chain edges in parallel with dense attention over all |
| vertices. A *physical attention bias* subtracts a distance, scaled by one learned |
| rate per head, from the attention logits. The variants differ only in which |
| distance each head receives: arc-length along the cable (chain), Euclidean |
| distance in space, both, or none. The model predicts per-vertex displacements |
| and is rolled out autoregressively. The block is d = 128, 8 heads, 4 layers |
| (891k parameters), trained 100k steps at batch 512. |
|
|
| ## What is here |
|
|
| The layout is the code repository's `results/runs/`. Each |
| `<experiment>/<label>/` folder holds the checkpoint |
| `checkpoints/step_100000.pt` and the run's record: the resolved `config.yaml`, |
| `env.json` (code version, library versions, GPU, timings), the training log |
| (`train_log.jsonl`, `train_log.txt`) and the unseen-test scorecard under |
| `eval/model/` (summary, per window, per cable, per frame). |
|
|
| | experiment | labels (seeds 0, 1, 2) | model | |
| |---|---|---| |
| | `local_on` | `varA_s*`, `varB_s*`, `varC_s*`, `varD_s*`, `varF_s*` | hybrid block: GatedGCN and global attention | |
| | `local_off` | the same | global attention only | |
| | `chain_only` | `chain_s*` | GatedGCN only, no attention | |
| | `structfree` | `mlp_pernode_s*`, `lstm_global_s*` | per-node MLP, global LSTM | |
|
|
| The records keep the experiment names and labels the runs had during |
| development (for example `b7_encoding`, `varF_enc_s0`). `provenance.csv` maps |
| each folder to its source run, the commit of the authors' development |
| repository it ran from, and whether that working tree had uncommitted changes. |
| Every checkpoint loads in the public code, and every cell of the paper's Tables |
| 1 and 2 is recomputed from these records. The local-on runs predate the phase |
| split that Table 2 reads, so each `local_on` folder also holds |
| `eval/rescore/`: its checkpoint re-scored with the public code |
| (`scripts/evaluate_final.sh`) on the released test root. |
|
|
| Variant letters, for the 8 heads: **A** Unbiased · **B** Euclidean on every |
| head · **C** Chain on every head · **D** Mixed (two heads Euclidean, two chain, |
| four unbiased) · **F** Euclidean+Chain only (four heads each). |
|
|
| ## Usage |
|
|
| ```bash |
| git clone https://github.com/avihaig/dlogps && cd dlogps && uv sync |
| scripts/fetch_checkpoints.sh # all 39 into results/runs/<experiment>/<label>/checkpoints/ |
| ``` |
|
|
| ```python |
| from dlogps.harness.train import load_checkpoint |
| |
| ckpt = load_checkpoint("results/runs/local_on/varF_s0/checkpoints/step_100000.pt") |
| model = ckpt.restore_model() # eval mode; the input normalization is ckpt.stats |
| ``` |
|
|
| To re-score a checkpoint on the paper's test set, fetch `unseen_cables_test` |
| from the [dataset](https://huggingface.co/datasets/avihaig/dlogps-cables), link |
| it with `scripts/link_data.sh`, and run |
| `scripts/evaluate_final.sh results/runs/local_on/varF_s0`. |
|
|
| Each file is a `torch.save` dictionary of tensors and plain values with no |
| pickled classes, so it loads with `torch.load(path, weights_only=True)`. |
|
|
| ## Results |
|
|
| Unseen-cable test, 400 windows of 400-frame rollouts. Cell: mean of seed means |
| ± mean of window sds ± sd of seed means; bold is the lowest mean in its block. |
| Lower is better. |
|
|
| | | rel ℓ2 (frac. of L) | link-length drift δ (frac. of ℓ0) | self-intersection v (frac. of frames) | |
| |---|---|---|---| |
| | *local stream on* | | | | |
| | Unbiased | 0.0325 ± 0.0281 ± 0.0058 | 0.0300 ± 0.0159 ± 0.0032 | 0.171 ± 0.231 ± 0.009 | |
| | Euclidean | 0.0320 ± 0.0267 ± 0.0042 | 0.0303 ± 0.0135 ± 0.0021 | 0.174 ± 0.229 ± 0.032 | |
| | Chain | 0.0316 ± 0.0341 ± 0.0057 | 0.0271 ± 0.0124 ± 0.0023 | 0.167 ± 0.227 ± 0.030 | |
| | Mixed | 0.0302 ± 0.0251 ± 0.0067 | **0.0267 ± 0.0109 ± 0.0030** | 0.168 ± 0.229 ± 0.012 | |
| | Euclidean+Chain only | **0.0297 ± 0.0288 ± 0.0035** | 0.0275 ± 0.0129 ± 0.0007 | **0.165 ± 0.229 ± 0.012** | |
| | *local stream off* | | | | |
| | Unbiased | 0.0399 ± 0.0246 ± 0.0002 | 0.1693 ± 0.1891 ± 0.0030 | 0.216 ± 0.258 ± 0.006 | |
| | Euclidean | 0.0382 ± 0.0279 ± 0.0007 | 0.1470 ± 0.2232 ± 0.0178 | 0.208 ± 0.262 ± 0.020 | |
| | Chain | **0.0339 ± 0.0195 ± 0.0008** | 0.0783 ± 0.0382 ± 0.0040 | 0.174 ± 0.241 ± 0.004 | |
| | Mixed | 0.0352 ± 0.0211 ± 0.0016 | **0.0779 ± 0.0455 ± 0.0036** | 0.172 ± 0.242 ± 0.016 | |
| | Euclidean+Chain only | 0.0343 ± 0.0202 ± 0.0002 | 0.0784 ± 0.0409 ± 0.0013 | **0.161 ± 0.234 ± 0.006** | |
| | *baselines* | | | | |
| | chain only | 0.0925 ± 0.0838 ± 0.0720 | 0.0812 ± 0.0589 ± 0.0322 | 0.115 ± 0.191 ± 0.024 | |
| | MLP | 0.0868 ± 0.0785 ± 0.0163 | 1.35 ± 2.32 ± 0.73 | 0.435 ± 0.256 ± 0.010 | |
| | LSTM | 0.1070 ± 0.0911 ± 0.0397 | 0.2819 ± 0.3124 ± 0.0509 | 0.082 ± 0.150 ± 0.031 | |
|
|
| ## Citation |
|
|
| ```bibtex |
| @inproceedings{giuili2026predicting, |
| title = {Predicting Cable Dynamics with Physical Attention Bias}, |
| author = {Giuili, Avihai and Atari, Rotem and Sintov, Avishai and Bechler-Speicher, Maya}, |
| booktitle = {NeurIPS 2026 Workshop on Symmetry and Geometry in Neural Representations (NeurReps)}, |
| year = {2026}, |
| eprint = {2610.11975}, |
| archivePrefix = {arXiv}, |
| primaryClass = {cs.RO}, |
| url = {https://arxiv.org/abs/2610.11975} |
| } |
| ``` |
|
|
| ## License |
|
|
| CC BY 4.0, the license of the paper. The code repository is MIT. |
