| # avoiding_baselines |
| |
| The ten diffusion planners of the paper's Table 3, run in DiRecT's constrained |
| `avoiding-v0` benchmark (D3IL, a planar reach past obstacles): Diffuser, Classifier |
| Guidance, Gradient Guidance, Projected Diffusion, Primal-Dual, Augmented Lagrangian, |
| SafeDiffuser-RoS, SafeDiffuser-ReS, SafeDiffuser-TVS and DiRecT. All of them sample from |
| one diffusion planner, trained here and shipped in `checkpoints/`. GlobMP has no row on |
| this task yet. |
| |
| ## Repository |
| |
| ``` |
| config/ machine paths (paths.env.example), the paper's settings (recipe.env), |
| the ten policies and the run behind each table row (policies.tsv) |
| data_prep/ fit_dynamics.sh: the linear dynamics from the 96 expert episodes |
| train/ train_diffusion.sh: the shared planner (skip it to use checkpoints/) |
| run/ run_baseline.sh: one policy; run_all.sh: all ten, Table 3 |
| eval/ RESULTS.md: the measured results and what not to compare |
| tools/ setup_avoiding_gpu.py: verifies the environment for this GPU |
| checkpoints/ model_ema_10.pth: the planner behind Table 3 (500k steps, 14 MB) |
| DiRecT/ DiRecT's d3il folder, unchanged, including the vendored dataset |
| ``` |
| |
| ## Original code |
| |
| `DiRecT/` is the `d3il/` folder of the original repository, unchanged (its license is kept |
| in the folder). Our additions live outside it. |
| |
| | Folder | Original repository | License | Our changes | |
| |---|---|---|---| |
| | `DiRecT/` | [azizanlab/DiRecT](https://github.com/azizanlab/DiRecT), `d3il/` (Giaretta, Li and Azizan, arXiv 2606.15359) | MIT | none; outside it: the launchers in `data_prep/`, `train/`, `run/`, `eval/RESULTS.md` and `tools/setup_avoiding_gpu.py` | |
| |
| ## Setup |
| |
| 1. `cp config/paths.env.example config/paths.env` and set `AB_PY`. |
| 2. Environment: `conda env create -f DiRecT/environment.yml` (env `direct-d3il`), then |
| `$AB_PY tools/setup_avoiding_gpu.py`. It checks that torch can drive this GPU -- |
| comparing the device's compute capability against torch's arch list, so it upgrades |
| only when it has to -- and reports anything missing. |
| 3. Nothing to download. The 96 expert episodes are vendored under |
| `DiRecT/d3il/environments/dataset/data/avoiding/`, and the planner is in |
| `checkpoints/`. Retraining it takes about a day; `train/train_diffusion.sh` does it. |
| |
| ## Use |
| |
| ``` |
| data_prep/fit_dynamics.sh # once, seconds on CPU |
| train/train_diffusion.sh [gpu] # optional: retrain the planner |
| run/run_baseline.sh <policy> [gpu] [episodes] |
| run/run_all.sh [gpu] # all ten, 100 episodes each |
| ``` |
| |
| Policies are listed in `config/policies.tsv`, which also names the run behind each row of |
| Table 3; the settings (seed 0, 100 episodes, checkpoint 10, twelve constraints) are in |
| `config/recipe.env`. |
| |
| ## Evaluation |
| |
| Each run plans with the policy and executes it in the simulator, replanning every four |
| executed steps for at most 100 steps. `eval.py` writes `runs.csv` (one row per episode), |
| `summary.json`, per-episode plots and `trajectories.npz`. |
| |
| **`D3IL_HARD=1` is what distinguishes this table.** It enables twelve constraints: the |
| nine of the shipped `novel` set plus the three keep-outs upstream ships commented out. |
| The nine-constraint runs are a different table -- DiRecT reads safety 0.97 there against |
| 0.87 here -- so the switch is set in `config/recipe.env` and passed by `run_baseline.sh`. |
|
|
| Two of the ten need `--guidance-last-steps 50` or they crash the simulator; their rows are |
| bracketed in the paper for that reason, and `run_baseline.sh` passes the flag for those two |
| only. `eval/RESULTS.md` has the measured numbers. |
|
|
| ## Outputs |
|
|
| ``` |
| <AB_ROOT>/runs/avoiding-v0/dynamics/linear_model.npz |
| <AB_ROOT>/runs/avoiding-v0/diffusion/x0pred/model_ema_*.pth (train_diffusion.sh) |
| <AB_ROOT>/runs/avoiding-v0/eval/<exp-name>/{runs.csv, summary.json, trajectories.npz, *.png} |
| <AB_ROOT>/runs/jobs.log |
| ``` |
|
|
