RoboChunk checkpoints

Independent re-implementation of H-Net-style dynamic chunking for robot trajectories. Not affiliated with the H-Net authors (Sukjun Hwang, Brandon Wang, Albert Gu) or Cartesia.

Checkpoints from RoboChunk, a pre-registered study of H-Net-style dynamic chunking on robot trajectories (FetchPickAndPlace-v4, scripted expert with labelled motion phases). Code, pre-registrations, decision log and the full report: https://github.com/NapoleonPK/robochunk-study. Simulated data and result files: https://huggingface.co/datasets/PAOP123/robochunk-sim

Results

test	criterion	key numbers (from runs/results_*.json)	verdict
H1	learned router − every baseline ≥ 0.10 boundary F1 (±3), CI excludes 0	learned 0.358 vs Fixed 0.594; difference −0.236 [−0.253, −0.219]	fail
H2	learned bits/step < Fixed at ≥ 2 of 3 N, compression matched	N=5: -13.165 vs -12.650; N=10: -13.150 vs -12.825; N=20: -13.208 vs -13.009 (compression 15.5 < 0.8 N, counts as fail)	pass
H3	≥ 2× chance near gripper events and p < 0.01, on both datasets	SO-100 1.89× (p = 0.001); Koch 1.77× (p = 0.001)	significant but below threshold
H4	Spearman ρ(novelty, gain) ≥ 0.6 over 11 clean candidates	ρ = 0.782 (p = 0.0045); 95% CI [0.25, 0.95] (post hoc); corrupted data scored most novel in 4 of 4	pass
H1-v2	refractory router − every baseline ≥ 0.10 raw F1 (fresh data)	vs Fixed +0.042 [+0.023, +0.061]	fail (predicted)
H1b-v2	chance-corrected (circular shift) difference > 0 vs every baseline, CI excludes 0	vs Fixed +0.103 [+0.084, +0.122]; smallest (vs Velocity minima) +0.055 [+0.038, +0.072]	pass
H3-v2	refractory router, ≥ 2× chance near gripper events, SO-100 and Koch	SO-100 1.53× (p = 0.001); Koch 1.52× (p = 0.001)	not supported
H3b-v2	same rule on a fresh dataset (SO-101, 5 test episodes)	1.74× (p = 0.004)	fail

True motion phases (bands) and the boundaries each rule places on six simulated test episodes, N = 10.

Full report with all numbers, caveats and deviations: REPORT.md in the GitHub repository.

Load a checkpoint and plot its boundaries

# Needs the code: git clone https://github.com/NapoleonPK/robochunk-study && cd robochunk-study && pip install -r requirements.txt
import matplotlib.pyplot as plt, torch, yaml
from huggingface_hub import snapshot_download
from robochunk.data import Normalizer
from robochunk.datasets import load_dataset
from robochunk.train import DEFAULTS, build_model, evaluate

run = "exp_refractory_N10_s0"  # any folder under runs/ in this repo
ckpt = snapshot_download("PAOP123/robochunk", allow_patterns=f"runs/{run}/*")
snapshot_download("PAOP123/robochunk-sim", repo_type="dataset", allow_patterns="sim/test.npz", local_dir="data")

d = f"{ckpt}/runs/{run}"
cfg = {**DEFAULTS, **yaml.safe_load(open(f"{d}/config.yaml"))}
eps = load_dataset({**cfg, "splits": {"test": "test"}})["test"]
model = build_model(cfg, eps[0]["x"].shape[1])
model.load_state_dict(torch.load(f"{d}/best.pt", map_location="cpu"))
ev = evaluate(model, cfg, eps[:1], None, Normalizer.load(f"{d}/norm.json"), torch.device("cpu"), keep=True)

ep, b = eps[0], ev["bounds"][0]
plt.step(range(len(ep["phase"])), ep["phase"], where="post", label="true phase")
plt.vlines(b[b > 0], -0.5, 6.5, color="C1", label="router boundaries")
plt.yticks(range(7), ["approach", "descend", "close", "lift", "transport", "open", "retreat"])
plt.xlabel("step (25 Hz)"); plt.legend(); plt.show()

Contents

runs/<run_id>/ for every simulated-data and Part B run:

• best.pt — PyTorch state dict at the best validation epoch; config.yaml — run settings; norm.json — input normalisation; metrics.json — test metrics and stop reason; history.json — training curve; git.txt — code commit at training time (an old hash: see History rewrite below).
• Run ids: sim_<method>_N<N>_s<seed> (main study), exp_refractory_N10_s* (refractory router, exploratory in v1, tested in v2), v2_sim_refractory_N10_s3–5 (v2 secondary seeds), abl_* (ablations), partb_* (models trained on the Part B reference set for novelty scoring), proxy_sim (entropy-baseline proxy). Real-robot checkpoints are not published.

Pre-registration hashes

SHA-256 of the pre-registration files at each tag and now (shasum -a 256 PREREGISTRATION*.md). PREREGISTRATION.md changed once, by Amendment 1, before any main result; neither file has changed since its last tag.

file	SHA-256	tag (commit)
PREREGISTRATION.md	e78668239e7401270c572d3feaef8d62cb2210e9d582ae640d525c94b52d8850	prereg (7ffc024)
PREREGISTRATION.md	628c6ad61a63b9ee08a84e2d4bf71d6ff3562c84c463cb87767c8c012ec86095	prereg-amend1 (fb5e2ad)
PREREGISTRATION.md	628c6ad61a63b9ee08a84e2d4bf71d6ff3562c84c463cb87767c8c012ec86095	prereg-v2 (9e99328)
PREREGISTRATION_v2.md	fa2700491bb7ca95826a0f81b868b538ebae54b47ba37d609a6e18ebd860c6b7	prereg-v2 (9e99328)
PREREGISTRATION.md	628c6ad61a63b9ee08a84e2d4bf71d6ff3562c84c463cb87767c8c012ec86095	current file
PREREGISTRATION_v2.md	fa2700491bb7ca95826a0f81b868b538ebae54b47ba37d609a6e18ebd860c6b7	current file

Timeline evidence

Timestamps recorded by third parties (UTC). Source details: docs/timeline_evidence.json in https://github.com/NapoleonPK/robochunk-study.

UTC	source	what it shows
2026-10-29 13:08:38	GitHub push event (earlier private repo)	push to main ending at old commit 1f1faa0 (= 7ffc024, tag prereg)
2026-10-29 13:08:51	Gmail, sent check-in email	check-in 1+2; its text quotes 'Pre-registration committed as 1f1faa0 (tag "prereg")'
2026-10-29 18:29:16	Gmail, sent check-in email	'Amendment 1 is committed and pushed (tag prereg-amend1). No main result existed before it.' (tag name, no hash)
2026-10-29 18:35:33	GitHub push event (earlier private repo)	push whose previous head was old commit f61d0f3 (= fb5e2ad, tag prereg-amend1), so that commit was on GitHub by then
2026-10-30 08:27:17	Gmail, sent check-in email	v1 results H1-H3 reported (v2 was designed after this, as PREREGISTRATION_v2.md states)
2026-10-30 08:42:19	Gmail, sent email	draft PREREGISTRATION_v2.md rev. 1 attached, for approval
2026-10-30 08:53:41	Gmail, sent email	draft PREREGISTRATION_v2.md rev. 2 attached, for approval (the approved rev. 3 was not emailed)
2026-10-30 09:42:58	GitHub (public repo created)	NapoleonPK/robochunk-study created with the rewritten history, including tag prereg-v2 (9e99328)
2026-10-30 10:39:31	GitHub Actions (public)	first CI run, on commit 53c390d, which has 9e99328 (tag prereg-v2) as an ancestor; the v2 training runs started after this
2026-10-30 13:42:56	GitHub Actions (public)	CI run on commit 9e1347d, which adds runs/results_v2.json (v2 analysis, single run)
2026-10-30 13:45:55	Gmail, sent check-in email	v2 results reported
2026-10-31 07:12:27	Hugging Face commit	dataset PAOP123/robochunk-sim, first upload (commit f509bea0ce)
2026-10-31 07:13:24	Hugging Face commit	model PAOP123/robochunk, first upload (commits be8a09a8f0, 9f7f28c5ac at 07:13:29Z)

Self-reported (git and local run logs, not independent): 2026-10-29 13:08:32 git commit 7ffc024 (old 1f1faa0), tag prereg; 2026-10-29 18:28:57 git commit fb5e2ad (old f61d0f3), tag prereg-amend1; 2026-10-29 18:29:02 first v1 main run started (runs/main_queue.log; no run had finished before the amendment); 2026-10-30 08:57:49 git commit 9e99328 (old d9b86c7), tag prereg-v2; 2026-10-30 08:58:28 git commit ac72fec: fresh simulated test split generated after the tag, SHA-256 recorded; 2026-10-30 11:14:00 v2 training runs started (runs/after_main.log, POLICIES_DONE 12:14 local time); 2026-10-30 13:40:00 v2 runs finished (V2_DONE 14:40 local time); analyze_v2.py run once afterwards.

History rewrite and commit map. Before the repository was made public (2026-10-30), its git history was rewritten once with git filter-repo --replace-text, only to replace the author's personal e-mail address in docs/BRIEF.md. File contents, dates, messages and tags are otherwise unchanged, so the pre-registration files have the same SHA-256 in the old and the new commits (checked for all three tags), but every commit hash changed. Emails, the earlier private repository and run git.txt files cite the old hashes; docs/HISTORY.md and docs/commit-map.txt map them to the new ones (for example 1f1faa0 → 7ffc024, tag prereg). Gmail: message 'date' from the Gmail API for messages sent from the author's account to the same address (verifiable by the account owner; originals with headers available on request). GitHub events: repository events API of the earlier private repository NapoleonPK/robochunk (visible to its owner while it exists). GitHub Actions and Hugging Face: public.

Credits

The model follows H-Net (Sukjun Hwang, Brandon Wang, Albert Gu, Dynamic Chunking for End-to-End Hierarchical Sequence Modeling, arXiv:2507.07955) and Cartesia's posts on dynamic chunking. Simulation: Gymnasium-Robotics and MuJoCo. Author: Téo Tran Van Lieu. Code written with Claude (Anthropic) as a coding agent.