EPCC: checkpoints and data

Checkpoints, evaluation datasets and published run directories for Expert-Play Contouring Control: Faster-than-Demonstration Planning from Slow Expert and Fast Play (arXiv:2609.22798). Code: github.com/epcc-robotics/epcc.

Download

hf download epcc-robotics/epcc --repo-type dataset --local-dir epcc_data   # about 100 GB
export STABLEWM_HOME=$PWD/epcc_data/data              # read by every evaluation cell
export EPCC_PUBLISHED=$PWD/epcc_data/published_runs   # read by the Table I / II scorers

To rescore the paper's runs without running any policy, download only published_runs/ (about 200 MB):

hf download epcc-robotics/epcc --repo-type dataset --local-dir epcc_data --include "published_runs/*"

Contents

data/ is the STABLEWM_HOME tree that the paper's evaluation cells (config/eval/cells/paper/ in the code repository) reference. docs/DATA.md in the code repository lists every file with the cells that use it. File names are the ones the cells were run with.

Path	Contents
data/checkpoints/agg_plainM2_object.ckpt, agg_slipM1_object.ckpt, agg_slipM2_object.ckpt, agg_cubeM2_object.ckpt	EPCC world models (PushT, Slippery-PushT, Cube), Table I and II
data/checkpoints/dtg/	contour generators (latent diffusion, one per task), Table I and II, Fig. 4, data scaling
data/checkpoints/pusht_wm/, slippery_pusht/lewm/, cube_wm/	base world models referenced by the baseline cells (pusht_wm/lewm_object.ckpt is the public LeWM PushT checkpoint)
data/checkpoints/dp/, skip/, sail/, data/cube/dp/, data/cube/sail/	Diffusion Policy, SkiP and SAIL baselines
data/{pusht,slippery_pusht,cube}/sup/	SuP baseline (ADM models and IQL gate)
data/pusht_invdyn/, data/slippery_pusht/invdyn_base/, data/cube/invdyn/	inverse-dynamics models (Table II)
data/checkpoints/fricd0*0_object.ckpt, data/checkpoints/friction/	world models and contour generators per friction level, Fig. 3a
data/checkpoints/f4cold/, wm_demoonly/, wm_playsub/	world models of the data-composition study, Fig. 4
data/checkpoints/scaling/	world models of the data-scaling study (appendix)
data/datasets/	evaluation datasets: slow expert demonstrations for PushT, Slippery-PushT and Cube (initial states and goals)
data/slippery_pusht/data/, data/{pusht,cube}/latents/	demonstration sets and their latent encodings used by the contour generators
data/{pusht,slippery_pusht,cube}/sail/awe/	SAIL waypoint labels
data/stats/	observation / action scaler statistics of each world model's training corpus (the corpora themselves are not included)
published_runs/findings/260907_final_dtg_s1_warm/	Table I (EPCC, Play+Goal) and Table II run directories and scores
published_runs/findings/261023_sup_robot/	Table I SuP run directories and scores
published_runs/findings/260521_baseline_setup/	baseline run directories read by the Table I scorer (Diffusion Policy, SkiP, SAIL at each k, SuP)

Each run directory holds rollout_traj.npz (states, actions and success flags of every episode), meta.json (the resolved configuration), the harness's results.json and, depending on the policy, control-timing traces and candidate decisions. The paper's success rates come from the scorers in the code repository (paper/common/score_runs.py), not from results.json.

Absolute paths recorded in configurations, checkpoint metadata and HDF5 attributes were shortened to ${STABLEWM_HOME}/…, ${REPO}/… and ${TMPDIR}/…, and the experiment-logging account name was replaced by <wandb-entity>; the paths document provenance and need not match this layout. The SAIL label files (data/*/sail/awe/*.h5) keep HDF5 external links to the images of the demonstration file they were labelled from; that file is not included and evaluation does not read these links.

License

MIT, as the code. The PushT world models build on the LeWorldModel PushT checkpoint (MIT).

Copyright (c) 2026 Lucas Maes
Copyright (c) 2026 the EPCC authors

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Citation

@article{cho2026epcc,
  title={Expert-Play Contouring Control: Faster-than-Demonstration Planning from Slow Expert and Fast Play},
  author={Seunghoon Cho and Wonsuhk Jung and Sundhar Vinodh Sangeetha and Shreyas Kousik},
  journal={arXiv preprint arXiv:2609.22798},
  year={2026}
}