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, `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

cp config/paths.env.example config/paths.env and set AB_PY.
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.
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