diff --git a/README.md b/README.md
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+---
+license: apache-2.0
+task_categories:
+- robotics
+tags:
+- LeRobot
+- so101
+- smolvla
+- teleoperation
+- egocentric
+- retargeting
+- ISR
+size_categories:
+- 10K<n<100K
+---
+
+# so101-smolvla-data — two SO-101 halves in LeRobot v3.0, ready for SmolVLA
+
+```
+std_mm_teleop_v30.tar    95 MB   100 eps    9,500 frames   1 task    ISR-standardized real teleop
+ego_v30.tar             319 MB   324 eps   36,442 frames  89 tasks   retargeted egocentric video
+std_results/                     the standardization run that produced the first half
+```
+
+Both tars unpack to a complete **LeRobot v3.0** tree (`meta/ data/ videos/`) that loads with
+`LeRobotDataset(repo_id, root=...)`. Same 6-DOF SO-101 layout, same camera keys, same units.
+
+## The two halves
+
+| Field | `std_mm_teleop_v30` | `ego_v30` |
+|---|---|---|
+| Format | LeRobot **v3.0** | LeRobot **v3.0** ✓ |
+| Robot | `so_follower` | `so101_follower` (same arm, different string) |
+| Rate | **10 fps** (30 Hz ÷ 3.05 ISR compression) | **30 fps** |
+| Episodes | **100** | **324** |
+| Frames | **9,500** (from 28,948) | **36,442** |
+| Tasks | **1** — "pick up blue cube and put into orange box" | **89** |
+| Duration | 15.8 min nominal (real span 16.1 min) | 20.2 min |
+| Episode length | mean 95, range 78–117 | mean 112.5, range 30–354 |
+| Camera keys | `observation.images.front` / `.wrist` | same ✓ |
+| Video | 640×480, h264, yuv420p | 640×480, h264, yuv420p ✓ |
+| Image source | **real** cameras (openbooth SO-101 rig), ISR-selected subset, re-encoded | **synthesized** — 2 crops from 1 ego camera |
+| state / action | `float32[6]` | `float32[6]` ✓ |
+| State layout | `[pan, lift, elbow, wrist_flex, wrist_roll, gripper]`, **degrees** | same ✓ |
+| Action convention | teleop **command**, leads state by ~4 raw frames (0.13 s, residual 0.90°); 2 steps / 2.08° after ISR | absolute **next-frame target** — exact, max abs diff **0.0** on all 324 eps |
+| Gripper encoding | deg, **larger = open** (checked against the wrist video: 1.40° = jaws touching, 59.78° = wide), range 1.4–59.8 | deg, **larger = open**, range −10.0–84.3 — same polarity ✓ |
+| Gripper occupancy | rests **closed** — mean 0.294 of span, 52.6 % of frames in the bottom quarter, 4.1 % in the top | rests **mid** — mean 0.328, 33.5 % bottom quarter, 0.7 % top |
+
+Every row is measured off the files (`std_results/stats_compare.py`, ffprobe on the mp4s, the
+parquet columns); raw numbers in `std_results/comparison_stats.json`.
+
+### What to watch when co-training
+
+1. **10 vs 30 fps.** ISR frames are non-uniform in real time, so no single fps is literally true;
+   10 is the measured effective rate and keeps episode durations within ~2 % of the real ones.
+   A shared action horizon in *seconds* therefore covers 3× more steps on the ego half.
+2. **Action convention.** On ego, `action[t] == state[t+1]` exactly — no controller dynamics to
+   learn. The teleop half carries a genuine ~0.13 s tracking lag, and ISR does not remove it.
+3. **Gripper span** differs (1.4–59.8° vs −10.0–84.3°) at identical polarity, so per-dataset
+   normalization differs; binarizing at each dataset's own closed→open midpoint is the safe fix.
+4. **1 vs 89 tasks**, real vs synthesized imagery.
+
+## Provenance
+
+| half | source | processing |
+|---|---|---|
+| `std_mm_teleop_v30` | `makermods/2nd_100ep_blue_cube_orange_box` (LeRobot v3.0, real SO-101 teleop) | ISR standardization (`teleop_std_poc`) → kept frames rewritten as a v3.0 dataset |
+| `ego_v30` | `angkul07/ego-data` (EgoDex), retargeted through DT-pipeline stage 6 run F | LeRobot v2.1 → v3.0 (metadata + ffmpeg concat, **stream copy** — pixels untouched) |
+
+## `std_results/` — the standardization run
+
+ISR (*Information-Standardized Trajectory Resampling*, Yang et al., IROS 2026,
+[arXiv:2606.22907](https://arxiv.org/abs/2606.22907)) keeps one frame per fixed amount of
+**information** (distance moved + accumulated acceleration) instead of one frame per fixed amount
+of time, so operator pauses collapse and contact-rich moments stay dense. Demonstration consistency
+is then scored with ActionVariance (Eq. 9 of
+[arXiv:2306.02437](https://arxiv.org/abs/2306.02437)) and episodes are bucketed.
+
+```
+out/report.json         final artifact: per-episode bucket + ISR stats + thresholds
+out/scores_raw.json     ActionVariance before ISR
+out/scores_isr.json     ActionVariance after ISR
+out/eps_sensitivity.json  cluster-radius stability check
+out/isr/                per-episode kept indices + resampled arrays + 3x uniform baseline
+out/plots/              compression / spacing / variance + 12 per-episode figures
+RUN_NOTES.md            knobs, calibration, results, caveats
+calibrate.py, eps_sens.py, build_std_dataset.py, stats_compare.py
+```
+
+**Results.** 28,948 → 9,500 frames (32.8 %). The kept ratio varies **25.1–44.1 %** per episode
+against a flat 33.4 % for the 3× time-uniform baseline — that content-adaptivity is ISR's claim,
+and it reproduces here. Buckets (P50/P90 on ISR scores): **50 train / 40 review / 10 quarantine**.
+
+Two caveats that belong next to those numbers:
+
+- **ActionVariance rose raw→ISR** (89.8 → 94.3, up in 76/100 episodes), the opposite of the
+  BridgeData POC. Expected rather than broken: dropping pause frames removes the *lowest*-variance
+  samples. Read the ISR column alone, as a within-dataset ranking.
+- **Buckets are ε-sensitive.** All 100 episodes are one task, so states overlap heavily; per-episode
+  rank correlation against ε=0.5 falls to 0.51 (ε=0.3), 0.41 (0.2), 0.19 (0.1). Treat the 10
+  quarantined episodes as a shortlist to eyeball, not a verdict.
+
+Knobs were recalibrated for degree units (the defaults assume metres): `d_target=12`,
+`λ_acc=0.002`, `gripper-threshold=2.0°`. See `RUN_NOTES.md`.
+
+The ActionVariance scorer is a from-the-paper implementation (the paper ships no code) and
+joint-space ISR is an extension beyond the paper, which operates on end-effector positions.
+
+## Use
+
+```bash
+huggingface-cli download angkul07/so101-smolvla-data --repo-type dataset --local-dir .
+tar -xf std_mm_teleop_v30.tar && tar -xf ego_v30.tar
+```
+
+```python
+from lerobot.datasets.lerobot_dataset import LeRobotDataset
+ds = LeRobotDataset("angkul07/so101-smolvla-data", root="std_mm_teleop_v30")
+```
diff --git a/ego_v30.tar b/ego_v30.tar
new file mode 100644
index 0000000000000000000000000000000000000000..2a1c3a47fe26b0cad9f4ec7ce1ac28906ac6b421
--- /dev/null
+++ b/ego_v30.tar
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8b3946a5ef64e225feadce8f6dddd22a0967e6c8a6a9ce2c9ae6e8c96698fc1c
+size 334131200
diff --git a/std_mm_teleop_v30.tar b/std_mm_teleop_v30.tar
new file mode 100644
index 0000000000000000000000000000000000000000..de21be5270ad954b9eeacd1161cc03e12c8f6510
--- /dev/null
+++ b/std_mm_teleop_v30.tar
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4f6390493354d38acb2f640c5eaea957c784ba509a0494fc92618c58641b5137
+size 98867200
diff --git a/std_results/RUN_NOTES.md b/std_results/RUN_NOTES.md
new file mode 100644
index 0000000000000000000000000000000000000000..cbd82f95fe04de0d298a1c5680f33046239108fd
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+++ b/std_results/RUN_NOTES.md
@@ -0,0 +1,98 @@
+# std_mm_teleop — teleop standardization run on `mm_teleop`
+
+Pipeline: `/workspace/teleop_std_poc` (ISR resample → ActionVariance score → bucket → plots).
+Source: `/workspace/mm_teleop` — LeRobot **v3.0**, `so_follower` (SO-101), 100 episodes,
+28 948 frames @ 30 fps, 1 task, 6-D state/action in **degrees** (5 arm joints + gripper).
+
+Run date: 2026-08-18. Env: `/workspace/.venv-tsp` (py3.10, numpy 2.2.6, pyarrow 25.0.1, mpl 3.10.9).
+
+## Command
+
+```bash
+cd /workspace/teleop_std_poc
+TSP_ROOT=/workspace/std_mm_teleop /workspace/.venv-tsp/bin/python run_all.py \
+    --local /workspace/mm_teleop --all-episodes \
+    --d-target 12 --lambda-acc 0.002 --gripper-threshold 2.0 --baseline 3 \
+    --eps 0.5 --max-episode-figs 12
+```
+
+## Pipeline changes this run needed (all backward compatible)
+
+| Change | Why |
+|---|---|
+| `TSP_ROOT` env var in all 5 stages | outputs land here instead of inside the repo |
+| `fetch_trajs.py --local <dir>` / `--all` | the POC only read the HF Hub; this dataset is on disk |
+| SO-101 6-D layout in `split_state` | 6-D state was falling through to `unknown[6]` → gripper force-keep disabled |
+| v3 parquet cache in `_episode_table_v3` | it re-read the 28 948-row concatenated file once per episode (100×) |
+| `isr_resample.py --gripper-threshold` | 0.05 is a *normalized* gripper default; this gripper is in degrees |
+| `viz.py --max-episode-figs`, wide-figure scaling | 100 bars/figures were unreadable |
+
+## Knob calibration (defaults assume metres — this data is degrees)
+
+Measured over all 100 episodes: per-frame joint step 2.03° mean / 1.50° median;
+accel magnitude 267 °/s² mean; gripper span 41.6° per episode.
+
+- `--lambda-acc 0.002` → the acceleration term carries **21 %** of per-frame info
+  (paper's 0.01-in-metres is likewise a minority-but-material share). 0.01 here would be 57 % — acc-dominated.
+- `--gripper-threshold 2.0` ≈ 5 % of the 41.6° gripper span — the same intent as the 0.05 normalized default.
+  Flags 12.5 % of frame pairs as gripper events (0.5 → 21 %, i.e. jitter).
+- `--d-target 12` → 32.8 % of frames kept, deliberately matched to the 3× uniform baseline (33.7 %)
+  so ISR-vs-uniform is like-for-like. Sweep is in `calibration_sweep.txt`.
+
+## Results
+
+**Compression** — 28 948 → 9 500 frames (32.8 %). Kept ratio varies **25.1 %–44.1 %** per episode
+(std 0.040); the 3× baseline is flat at 33.4 % (std 0.001). That content-adaptivity is the ISR claim,
+and it reproduces here. 42 frames/episode on average are gripper-forced.
+
+**Scoring** — dataset ActionVariance **raw 89.8 → ISR 94.3 (+5.0 %)**; it *rose* in 76/100 episodes
+(mean +5.4 %). This is the opposite of the BridgeData POC (−22 %) and is expected rather than a bug:
+removing pause frames removes the *lowest*-variance samples (a held pose has near-identical actions),
+so the mean over what remains goes up. The raw→ISR delta is therefore **not** a "pacing noise removed"
+readout on this dataset — read the ISR column alone, as a relative ranking.
+
+**Buckets** (percentile mode, P50/P90 on ISR scores): **50 train / 40 review / 10 quarantine**.
+Quarantined: `ep7, ep11, ep15, ep42, ep61, ep71, ep83, ep86, ep87, ep92`.
+Score spread 71.0 → 115.1 (p50 95.0, p90 106.1); every episode was scoreable (100 % clustered coverage).
+
+## Caveats
+
+1. **Bucket assignment is ε-sensitive on this dataset** (`out/eps_sensitivity.json`). All 100 episodes
+   are the same task, so states overlap heavily. Per-episode rank correlation against ε=0.5:
+   ε=0.3 → 0.51, ε=0.2 → 0.41, ε=0.1 → 0.19; mean cluster size 90 → 22 → 8.5 → 3.5 points.
+   The train/quarantine split would reshuffle materially at a tighter radius — treat the current
+   10 quarantines as a shortlist to eyeball, not a verdict, until ε is pinned by a labeled reference.
+2. Scores are in **degrees²** and only comparable within this dataset (the scorer is test-grade —
+   implemented from paper Eq. 9, no official code exists).
+3. Joint-space ISR is the POC's extension beyond the paper (paper operates on EE positions).
+4. Only 5 arm joints drive the resampling; the gripper channel drives force-keeping only.
+
+## Outputs
+
+```
+trajs/               100 × ep{N}.npz (positions/gripper/actions/timestamps) + index.json
+out/isr/             100 × ep{N}.npz (ISR-kept) + ep{N}_baseline3x.npz + stats.json
+out/scores_raw.json  ActionVariance before ISR
+out/scores_isr.json  ActionVariance after ISR
+out/report.json      final artifact — buckets, thresholds, per-episode ISR stats
+out/eps_sensitivity.json   ε-radius stability check
+out/plots/           compression.png, spacing.png, variance.png, ep{0..11}_isr.png
+run.log              full stdout
+```
+
+Nothing was written back into `mm_teleop`; it is untouched.
+
+---
+
+## v3.0 dataset build (2026-08-18, after the POC run)
+
+`build_std_dataset.py` materializes the ISR-kept frames as a real LeRobot **v3.0** dataset at
+`lerobot_v30/` (223 MB): parquet rows AND the matching video frames, decoded from `mm_teleop`
+and re-encoded h264. 100 eps / 9,500 frames / 1 task, loads via `LeRobotDataset`.
+
+Frames are renumbered onto a uniform grid at **10 fps** — the measured effective rate
+(30 fps ÷ 3.05x compression) — because ISR spacing is non-uniform in real time and LeRobot
+enforces `timestamp ≈ frame_index / fps`. Episode durations stay within ~2% of the real ones.
+`--fps 30` rebuilds at the source rate instead.
+
+Env: `/workspace/.venv-lerobot` (lerobot 0.4.4). Comparison against ego: `COMPARISON_ego_vs_std_mm_teleop.md`.
diff --git a/std_results/build_std_dataset.py b/std_results/build_std_dataset.py
new file mode 100644
index 0000000000000000000000000000000000000000..96440e94da9138a7f6610d524c408f9bd19f1292
--- /dev/null
+++ b/std_results/build_std_dataset.py
@@ -0,0 +1,149 @@
+#!/usr/bin/env python3
+"""Materialize the ISR-standardized teleop set as a native LeRobot v3.0 dataset.
+
+`teleop_std_poc` only emits npz + report artifacts; SmolVLA needs a real dataset. This takes
+    source frames : /workspace/mm_teleop            (LeRobot v3.0, SO-101, 30 fps)
+    kept indices  : /workspace/std_mm_teleop/out/isr/ep{N}.npz  ["keep"]
+and writes the kept frames — parquet rows AND the matching video frames — as a new v3.0 tree.
+
+TIME: ISR frames are non-uniform in real time, so the original timestamps cannot be carried over
+(LeRobot enforces timestamp ≈ frame_index / fps within tolerance_s). Frames are renumbered onto a
+uniform grid at the dataset's EFFECTIVE rate (30 fps / mean compression ≈ 10 fps), which keeps
+episode durations within a few percent of the real ones. Pass --fps 30 to renumber at the source
+rate instead (episodes then play ~3x fast).
+
+Videos are decoded sequentially per camera file (episodes are contiguous and ordered inside them),
+so each source frame is touched exactly once.
+"""
+import argparse
+import json
+import os
+import time
+from pathlib import Path
+
+os.environ.setdefault("HF_HUB_OFFLINE", "1")
+os.environ.setdefault("HF_DATASETS_OFFLINE", "1")
+
+import av
+import numpy as np
+import pyarrow.parquet as pq
+
+from lerobot.datasets.lerobot_dataset import LeRobotDataset
+
+SRC = Path("/workspace/mm_teleop")
+ISR = Path("/workspace/std_mm_teleop/out/isr")
+OUT = Path("/workspace/std_mm_teleop/lerobot_v30")
+
+
+class CamReader:
+    """Sequential decoder over one camera's concatenated v3.0 video files."""
+
+    def __init__(self, root: Path, cam: str):
+        self.dir = root / "videos" / cam / "chunk-000"
+        self.file_index = None
+        self.container = None
+        self.iter = None
+
+    def _open(self, file_index: int):
+        if self.container is not None:
+            self.container.close()
+        path = self.dir / f"file-{file_index:03d}.mp4"
+        self.container = av.open(str(path))
+        self.iter = self.container.decode(video=0)
+        self.file_index = file_index
+
+    def take(self, file_index: int, count: int, keep_local: set[int]) -> dict[int, np.ndarray]:
+        """Consume `count` frames of that file, returning only the ones in keep_local."""
+        if file_index != self.file_index:
+            self._open(file_index)
+        out = {}
+        for i in range(count):
+            frame = next(self.iter)
+            if i in keep_local:
+                out[i] = frame.to_ndarray(format="rgb24")
+        return out
+
+    def close(self):
+        if self.container is not None:
+            self.container.close()
+
+
+def main():
+    ap = argparse.ArgumentParser()
+    ap.add_argument("--fps", type=int, default=0, help="0 = effective rate (source fps / compression)")
+    ap.add_argument("--repo-id", default="angkul07/std_mm_teleop")
+    ap.add_argument("--out", default=str(OUT))
+    ap.add_argument("--limit", type=int, default=0, help="debug: only N episodes")
+    a = ap.parse_args()
+
+    out_root = Path(a.out)
+    if out_root.exists():
+        raise SystemExit(f"{out_root} exists — remove it first")
+
+    info = json.loads((SRC / "meta" / "info.json").read_text())
+    src_fps = int(info["fps"])
+    features = {k: dict(v, shape=tuple(v["shape"]))   # info.json stores lists; validation wants tuples
+                for k, v in info["features"].items()
+                if k in ("action", "observation.state") or k.startswith("observation.images")}
+    cams = [k for k in features if k.startswith("observation.images")]
+
+    ep_meta = pq.read_table(SRC / "meta" / "episodes" / "chunk-000" / "file-000.parquet").to_pydict()
+    n_eps = len(ep_meta["episode_index"])
+    data = pq.read_table(SRC / "data" / "chunk-000" / "file-000.parquet")
+    state_all = np.array(data["observation.state"].to_pylist(), dtype=np.float32)
+    action_all = np.array(data["action"].to_pylist(), dtype=np.float32)
+    task_by_index = dict(zip(*pq.read_table(SRC / "meta" / "tasks.parquet").to_pydict().values()))
+    task_index_all = np.array(data["task_index"].to_pylist())
+
+    keeps = {}
+    for ep in range(n_eps):
+        keeps[ep] = np.load(ISR / f"ep{ep}.npz")["keep"].astype(int)
+    kept_total = sum(len(v) for v in keeps.values())
+    src_total = int(info["total_frames"])
+    compression = src_total / kept_total
+    fps = a.fps or max(1, round(src_fps / compression))
+    print(f"{src_total} -> {kept_total} frames ({100*kept_total/src_total:.1f}%), "
+          f"compression {compression:.2f}x -> writing at {fps} fps "
+          f"({'effective rate' if not a.fps else 'forced'})")
+
+    ds = LeRobotDataset.create(
+        repo_id=a.repo_id, fps=fps, features=features, root=out_root,
+        robot_type=info.get("robot_type"), use_videos=True,
+        image_writer_processes=0, image_writer_threads=4, vcodec="h264",
+    )
+
+    readers = {c: CamReader(SRC, c) for c in cams}
+    t0 = time.time()
+    todo = n_eps if not a.limit else min(a.limit, n_eps)
+    for ep in range(todo):
+        lo, hi = ep_meta["dataset_from_index"][ep], ep_meta["dataset_to_index"][ep]
+        length = hi - lo
+        keep = keeps[ep]
+        keep_set = set(keep.tolist())
+        frames = {}
+        for cam in cams:
+            fi = ep_meta[f"videos/{cam}/file_index"][ep]
+            frames[cam] = readers[cam].take(fi, length, keep_set)
+            if len(frames[cam]) != len(keep):
+                raise SystemExit(f"ep{ep} {cam}: got {len(frames[cam])} frames, expected {len(keep)}")
+        task = task_by_index[int(task_index_all[lo])]
+        for j in keep:
+            ds.add_frame({
+                "observation.state": state_all[lo + j],
+                "action": action_all[lo + j],
+                **{c: frames[c][int(j)] for c in cams},
+                "task": task,
+            })
+        ds.save_episode()
+        if ep % 10 == 0 or ep == todo - 1:
+            el = time.time() - t0
+            print(f"  ep{ep:>3}: {length} -> {len(keep)} frames | {el:.0f}s elapsed, "
+                  f"eta {el/(ep+1)*(todo-ep-1):.0f}s", flush=True)
+
+    for r in readers.values():
+        r.close()
+    print(f"done in {time.time()-t0:.0f}s -> {out_root}")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/std_results/build_v30.log.gz b/std_results/build_v30.log.gz
new file mode 100644
index 0000000000000000000000000000000000000000..6c49b4158828881daeb14ddaff2a9c80e6637d1b
--- /dev/null
+++ b/std_results/build_v30.log.gz
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:bcc356159e6c0c7a67dc4de1459cc5782414cc4577bfd7f40a60d3bfc1e6e611
+size 25508
diff --git a/std_results/calibrate.py b/std_results/calibrate.py
new file mode 100644
index 0000000000000000000000000000000000000000..90fc3c6f10f51ea74b2df01d11631e9dce921a6c
--- /dev/null
+++ b/std_results/calibrate.py
@@ -0,0 +1,51 @@
+"""Calibrate ISR knobs for degree-unit SO-101 joint data (defaults assume metres)."""
+import sys, json
+from pathlib import Path
+import numpy as np
+
+sys.path.insert(0, "/workspace/teleop_std_poc")
+from isr_resample import _acceleration_magnitudes, isr_resample, _gripper_forced
+
+T = Path("/workspace/std_mm_teleop/trajs")
+files = sorted(T.glob("ep*.npz"), key=lambda p: int(p.stem[2:]))
+
+step_all, acc_all, grip_d, spans = [], [], [], []
+for f in files:
+    d = np.load(f)
+    pos, ts, g = d["positions"], d["timestamps"], d["gripper"]
+    step_all.append(np.linalg.norm(np.diff(pos, axis=0), axis=1))
+    acc_all.append(_acceleration_magnitudes(pos, ts))
+    grip_d.append(np.abs(np.diff(g)))
+    spans.append(g.max() - g.min())
+step = np.concatenate(step_all); acc = np.concatenate(acc_all); gd = np.concatenate(grip_d)
+print(f"per-frame joint step (deg): mean {step.mean():.3f} median {np.median(step):.3f} p90 {np.percentile(step,90):.3f}")
+print(f"accel magnitude (deg/s^2): mean {acc.mean():.1f} median {np.median(acc):.1f}")
+print(f"gripper |delta| per frame: median {np.median(gd):.3f} p90 {np.percentile(gd,90):.3f} max {gd.max():.2f}")
+print(f"gripper span per episode : mean {np.mean(spans):.1f}")
+
+# lambda_acc so the acceleration term carries a comparable weight to distance, as in the paper
+# (paper: metres + lambda_acc 0.01 -> acc term is a minority but non-trivial share of the info)
+for la in [0.0, 0.001, 0.002, 0.005, 0.01]:
+    share = la * acc.mean() / (step.mean() + la * acc.mean())
+    print(f"  lambda_acc={la:<6} -> acc share of per-frame info {100*share:.0f}%")
+
+# gripper threshold: pick the knee between "real open/close" and per-frame jitter
+for th in [0.5, 1.0, 2.0, 3.0]:
+    frac = float((gd > th).mean())
+    print(f"  grip_threshold={th:<4} -> {100*frac:.1f}% of frame pairs flagged as gripper events")
+
+# d_target sweep on a 12-episode probe (keep ratio + gripper-forced share)
+probe = files[:12]
+la, gt = 0.002, 2.0
+print("\nd_target sweep (12-episode probe, lambda_acc=%.3f, grip_th=%.1f):" % (la, gt))
+for dt in [2, 4, 6, 8, 12, 16, 24]:
+    ratios, forced_share = [], []
+    for f in probe:
+        d = np.load(f)
+        pos, ts, g = d["positions"], d["timestamps"], d["gripper"]
+        keep = isr_resample(pos, ts, g, dt, 1.0, la, gt)
+        forced = _gripper_forced(g, len(pos), threshold=gt)
+        ratios.append(len(keep) / len(pos))
+        forced_share.append(len(forced) / max(len(keep), 1))
+    print(f"  d_target={dt:<4} kept {100*np.mean(ratios):5.1f}% (min {100*min(ratios):.0f} max {100*max(ratios):.0f}) "
+          f"| gripper-forced share of kept {100*np.mean(forced_share):.0f}%")
diff --git a/std_results/calibration_sweep.txt b/std_results/calibration_sweep.txt
new file mode 100644
index 0000000000000000000000000000000000000000..f435c13a05effed0d32670ee79947ba5cedcb410
--- /dev/null
+++ b/std_results/calibration_sweep.txt
@@ -0,0 +1,22 @@
+per-frame joint step (deg): mean 2.028 median 1.497 p90 4.997
+accel magnitude (deg/s^2): mean 267.4 median 237.4
+gripper |delta| per frame: median 0.000 p90 2.478 max 3.75
+gripper span per episode : mean 41.6
+  lambda_acc=0.0    -> acc share of per-frame info 0%
+  lambda_acc=0.001  -> acc share of per-frame info 12%
+  lambda_acc=0.002  -> acc share of per-frame info 21%
+  lambda_acc=0.005  -> acc share of per-frame info 40%
+  lambda_acc=0.01   -> acc share of per-frame info 57%
+  grip_threshold=0.5  -> 21.3% of frame pairs flagged as gripper events
+  grip_threshold=1.0  -> 18.0% of frame pairs flagged as gripper events
+  grip_threshold=2.0  -> 12.5% of frame pairs flagged as gripper events
+  grip_threshold=3.0  -> 7.3% of frame pairs flagged as gripper events
+
+d_target sweep (12-episode probe, lambda_acc=0.002, grip_th=2.0):
+  d_target=2    kept  69.7% (min 65 max 74) | gripper-forced share of kept 21%
+  d_target=4    kept  60.5% (min 53 max 65) | gripper-forced share of kept 24%
+  d_target=6    kept  51.5% (min 41 max 58) | gripper-forced share of kept 29%
+  d_target=8    kept  42.9% (min 34 max 49) | gripper-forced share of kept 34%
+  d_target=12   kept  33.3% (min 26 max 42) | gripper-forced share of kept 44%
+  d_target=16   kept  28.8% (min 22 max 38) | gripper-forced share of kept 51%
+  d_target=24   kept  23.9% (min 18 max 34) | gripper-forced share of kept 61%
diff --git a/std_results/comparison_stats.json b/std_results/comparison_stats.json
new file mode 100644
index 0000000000000000000000000000000000000000..107e162689d6d48c6cdd7693d3effd385d4cbffb
--- /dev/null
+++ b/std_results/comparison_stats.json
@@ -0,0 +1,224 @@
+[
+  {
+    "name": "std_mm_teleop",
+    "root": "/workspace/std_mm_teleop/lerobot_v30",
+    "format": "LeRobot v3.0",
+    "robot_type": "so_follower",
+    "fps": 10,
+    "episodes": 100,
+    "frames": 9500,
+    "tasks": 1,
+    "task_examples": [
+      "pick up blue cube and put into orange box"
+    ],
+    "duration_s": 950.0,
+    "duration_hms": "15.8 min",
+    "episode_len": {
+      "mean": 95.0,
+      "min": 78,
+      "max": 117,
+      "median": 94
+    },
+    "camera_keys": [
+      "observation.images.wrist",
+      "observation.images.front"
+    ],
+    "video": {
+      "observation.images.wrist": {
+        "codec": "h264",
+        "size": "640x480",
+        "pix_fmt": "yuv420p",
+        "fps": "10/1",
+        "n_files": 1
+      },
+      "observation.images.front": {
+        "codec": "h264",
+        "size": "640x480",
+        "pix_fmt": "yuv420p",
+        "fps": "10/1",
+        "n_files": 1
+      }
+    },
+    "state_dtype": "float32[6]",
+    "action_dtype": "float32[6]",
+    "state_layout": [
+      "shoulder_pan.pos",
+      "shoulder_lift.pos",
+      "elbow_flex.pos",
+      "wrist_flex.pos",
+      "wrist_roll.pos",
+      "gripper.pos"
+    ],
+    "state_range_deg": [
+      [
+        -14.3,
+        62.6
+      ],
+      [
+        -105.8,
+        70.7
+      ],
+      [
+        -78.3,
+        97.1
+      ],
+      [
+        28.7,
+        97.2
+      ],
+      [
+        -72.2,
+        115.5
+      ],
+      [
+        1.4,
+        59.8
+      ]
+    ],
+    "action_convention": {
+      "mae_vs_state_at_lag": {
+        "0": 4.0875,
+        "1": 2.4818,
+        "2": 2.0846,
+        "3": 3.0765,
+        "4": 4.45,
+        "5": 6.1464,
+        "6": 7.921
+      },
+      "best_lag_frames": 2,
+      "best_lag_mae_deg": 2.0846
+    },
+    "gripper": {
+      "state_range": [
+        1.398,
+        59.784
+      ],
+      "action_range": [
+        0.0,
+        60.714
+      ],
+      "looks_normalized_01": false,
+      "mean": 18.569,
+      "mean_normalized": 0.294,
+      "p05_p50_p95": [
+        1.461,
+        13.85,
+        44.473
+      ],
+      "pct_below_25pct_of_span": 52.6,
+      "pct_above_75pct_of_span": 4.1
+    }
+  },
+  {
+    "name": "ego_v21",
+    "root": "/workspace/final_dataset/final_data/ego_v21",
+    "format": "LeRobot v3.0",
+    "robot_type": "so101_follower",
+    "fps": 30,
+    "episodes": 324,
+    "frames": 36442,
+    "tasks": 89,
+    "task_examples": [
+      "pick up a AirPods case from the table and place it on the chessboard.",
+      "pick up a black silk hairtie from the table and place it in the clear jar.",
+      "pick up a black stapler from the table and place it in the box lid."
+    ],
+    "duration_s": 1214.7,
+    "duration_hms": "20.2 min",
+    "episode_len": {
+      "mean": 112.5,
+      "min": 30,
+      "max": 354,
+      "median": 96
+    },
+    "camera_keys": [
+      "observation.images.front",
+      "observation.images.wrist"
+    ],
+    "video": {
+      "observation.images.front": {
+        "codec": "h264",
+        "size": "640x480",
+        "pix_fmt": "yuv420p",
+        "fps": "30/1",
+        "n_files": 1
+      },
+      "observation.images.wrist": {
+        "codec": "h264",
+        "size": "640x480",
+        "pix_fmt": "yuv420p",
+        "fps": "30/1",
+        "n_files": 1
+      }
+    },
+    "state_dtype": "float32[6]",
+    "action_dtype": "float32[6]",
+    "state_layout": [
+      "shoulder_pan.pos",
+      "shoulder_lift.pos",
+      "elbow_flex.pos",
+      "wrist_flex.pos",
+      "wrist_roll.pos",
+      "gripper.pos"
+    ],
+    "state_range_deg": [
+      [
+        -110.0,
+        110.0
+      ],
+      [
+        -100.0,
+        100.0
+      ],
+      [
+        -96.8,
+        96.8
+      ],
+      [
+        -95.0,
+        95.0
+      ],
+      [
+        -157.2,
+        162.8
+      ],
+      [
+        -10.0,
+        84.3
+      ]
+    ],
+    "action_convention": {
+      "mae_vs_state_at_lag": {
+        "0": 1.9507,
+        "1": 0.0,
+        "2": 1.9761,
+        "3": 3.8321,
+        "4": 5.6246,
+        "5": 7.3562,
+        "6": 9.0061
+      },
+      "best_lag_frames": 1,
+      "best_lag_mae_deg": 0.0
+    },
+    "gripper": {
+      "state_range": [
+        -10.0,
+        84.298
+      ],
+      "action_range": [
+        -8.594,
+        84.298
+      ],
+      "looks_normalized_01": false,
+      "mean": 20.944,
+      "mean_normalized": 0.328,
+      "p05_p50_p95": [
+        1.1,
+        18.343,
+        49.112
+      ],
+      "pct_below_25pct_of_span": 33.5,
+      "pct_above_75pct_of_span": 0.7
+    }
+  }
+]
\ No newline at end of file
diff --git a/std_results/eps_sens.py b/std_results/eps_sens.py
new file mode 100644
index 0000000000000000000000000000000000000000..2718d0f04ca3854dc77c0b3598cfa6e0320b0ba7
--- /dev/null
+++ b/std_results/eps_sens.py
@@ -0,0 +1,43 @@
+"""ε sensitivity: is the per-episode ranking stable, or an artifact of over-wide state clusters?"""
+import sys, json
+from pathlib import Path
+import numpy as np
+
+sys.path.insert(0, "/workspace/teleop_std_poc")
+import os
+os.environ.setdefault("TSP_ROOT", "/workspace/std_mm_teleop")
+from action_variance import load, score
+
+eps_list = [0.5, 0.3, 0.2, 0.1]
+episodes = load("isr")
+S = np.concatenate([p for _, p, _ in episodes])
+mu, sd = S.mean(0), S.std(0) + 1e-9
+Z = (S - mu) / sd
+res = {}
+rank0 = None
+out = {}
+for e in eps_list:
+    per, ds, _ = score(episodes, e, 2)
+    names = list(per)
+    vals = np.array([per[n]["score"] if per[n]["score"] is not None else np.nan for n in names])
+    cov = np.mean([per[n]["scored_points"] / per[n]["total_points"] for n in names])
+    # mean cluster size, sampled (full N^2 already done inside score, recompute cheaply on a sample)
+    idx = np.random.default_rng(0).choice(len(Z), size=min(2000, len(Z)), replace=False)
+    sizes = [(np.linalg.norm(Z - Z[i], axis=1) <= e).sum() for i in idx]
+    r = np.argsort(np.argsort(np.nan_to_num(vals, nan=-1)))
+    if rank0 is None:
+        rank0 = r
+        corr = 1.0
+    else:
+        corr = float(np.corrcoef(rank0, r)[0, 1])
+    out[str(e)] = {"dataset_av": None if ds is None else round(ds, 4),
+                   "coverage": round(100 * cov, 1),
+                   "mean_cluster_size": round(float(np.mean(sizes)), 1),
+                   "median_cluster_size": int(np.median(sizes)),
+                   "cluster_pct_of_dataset": round(100 * float(np.mean(sizes)) / len(Z), 2),
+                   "rank_corr_vs_eps0.5": round(corr, 3)}
+    print(e, out[str(e)])
+Path("/workspace/std_mm_teleop/out/eps_sensitivity.json").write_text(json.dumps(
+    {"note": "per-episode ActionVariance ranking vs cluster radius on ISR-resampled data",
+     "n_points": int(len(Z)), "eps": out}, indent=2))
+print("wrote out/eps_sensitivity.json")
diff --git a/std_results/out/eps_sensitivity.json b/std_results/out/eps_sensitivity.json
new file mode 100644
index 0000000000000000000000000000000000000000..579e60018413817343aa968f4c3c96afe184912f
--- /dev/null
+++ b/std_results/out/eps_sensitivity.json
@@ -0,0 +1,38 @@
+{
+  "note": "per-episode ActionVariance ranking vs cluster radius on ISR-resampled data",
+  "n_points": 9500,
+  "eps": {
+    "0.5": {
+      "dataset_av": 94.3317,
+      "coverage": 99.6,
+      "mean_cluster_size": 90.5,
+      "median_cluster_size": 34,
+      "cluster_pct_of_dataset": 0.95,
+      "rank_corr_vs_eps0.5": 1.0
+    },
+    "0.3": {
+      "dataset_av": 32.7051,
+      "coverage": 92.4,
+      "mean_cluster_size": 22.1,
+      "median_cluster_size": 11,
+      "cluster_pct_of_dataset": 0.23,
+      "rank_corr_vs_eps0.5": 0.51
+    },
+    "0.2": {
+      "dataset_av": 11.1128,
+      "coverage": 74.0,
+      "mean_cluster_size": 8.5,
+      "median_cluster_size": 5,
+      "cluster_pct_of_dataset": 0.09,
+      "rank_corr_vs_eps0.5": 0.41
+    },
+    "0.1": {
+      "dataset_av": 2.0393,
+      "coverage": 42.3,
+      "mean_cluster_size": 3.5,
+      "median_cluster_size": 1,
+      "cluster_pct_of_dataset": 0.04,
+      "rank_corr_vs_eps0.5": 0.186
+    }
+  }
+}
\ No newline at end of file
diff --git a/std_results/out/isr/ep0.npz b/std_results/out/isr/ep0.npz
new file mode 100644
index 0000000000000000000000000000000000000000..99e450ecdec57173fc9c64f5b4c31977d5ed7469
--- /dev/null
+++ b/std_results/out/isr/ep0.npz
@@ -0,0 +1,3 @@
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+size 11892
diff --git a/std_results/out/isr/ep0_baseline3x.npz b/std_results/out/isr/ep0_baseline3x.npz
new file mode 100644
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--- /dev/null
+++ b/std_results/out/isr/ep0_baseline3x.npz
@@ -0,0 +1,3 @@
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new file mode 100644
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--- /dev/null
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diff --git a/std_results/out/isr/ep10.npz b/std_results/out/isr/ep10.npz
new file mode 100644
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--- /dev/null
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new file mode 100644
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--- /dev/null
+++ b/std_results/out/isr/ep10_baseline3x.npz
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new file mode 100644
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new file mode 100644
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new file mode 100644
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--- /dev/null
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new file mode 100644
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new file mode 100644
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new file mode 100644
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new file mode 100644
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new file mode 100644
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new file mode 100644
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new file mode 100644
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diff --git a/std_results/out/isr/ep16.npz b/std_results/out/isr/ep16.npz
new file mode 100644
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+        "ep67"
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+    }
+  ],
+  "note": "TEST-GRADE: implemented from paper Eq.9 (no official code exists); scores are relative within-dataset, not calibrated absolutes."
+}
\ No newline at end of file
diff --git a/std_results/out/scores_isr.json b/std_results/out/scores_isr.json
new file mode 100644
index 0000000000000000000000000000000000000000..5341a1dc7548441202699af67768ad57103e5a11
--- /dev/null
+++ b/std_results/out/scores_isr.json
@@ -0,0 +1,660 @@
+{
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+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/std_results/out/scores_raw.json b/std_results/out/scores_raw.json
new file mode 100644
index 0000000000000000000000000000000000000000..5fe807abfded3797ff0199e034fe03a743b58a98
--- /dev/null
+++ b/std_results/out/scores_raw.json
@@ -0,0 +1,664 @@
+{
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+    "eps": 0.5,
+    "min_cluster": 2
+  },
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+      "scored_points": 300,
+      "total_points": 300
+    },
+    "ep20": {
+      "score": 79.103303,
+      "scored_points": 297,
+      "total_points": 297
+    },
+    "ep21": {
+      "score": 73.820294,
+      "scored_points": 281,
+      "total_points": 281
+    },
+    "ep22": {
+      "score": 81.775026,
+      "scored_points": 255,
+      "total_points": 255
+    },
+    "ep23": {
+      "score": 80.356488,
+      "scored_points": 278,
+      "total_points": 278
+    },
+    "ep24": {
+      "score": 83.348766,
+      "scored_points": 251,
+      "total_points": 251
+    },
+    "ep25": {
+      "score": 95.220908,
+      "scored_points": 266,
+      "total_points": 266
+    },
+    "ep26": {
+      "score": 96.067244,
+      "scored_points": 238,
+      "total_points": 238
+    },
+    "ep27": {
+      "score": 100.979283,
+      "scored_points": 262,
+      "total_points": 262
+    },
+    "ep28": {
+      "score": 89.230335,
+      "scored_points": 308,
+      "total_points": 308
+    },
+    "ep29": {
+      "score": 83.641057,
+      "scored_points": 270,
+      "total_points": 270
+    },
+    "ep3": {
+      "score": 91.383367,
+      "scored_points": 291,
+      "total_points": 291
+    },
+    "ep30": {
+      "score": 86.083411,
+      "scored_points": 275,
+      "total_points": 275
+    },
+    "ep31": {
+      "score": 84.795859,
+      "scored_points": 278,
+      "total_points": 278
+    },
+    "ep32": {
+      "score": 100.813031,
+      "scored_points": 273,
+      "total_points": 273
+    },
+    "ep33": {
+      "score": 85.617808,
+      "scored_points": 291,
+      "total_points": 291
+    },
+    "ep34": {
+      "score": 84.7752,
+      "scored_points": 266,
+      "total_points": 266
+    },
+    "ep35": {
+      "score": 86.533757,
+      "scored_points": 290,
+      "total_points": 290
+    },
+    "ep36": {
+      "score": 99.057732,
+      "scored_points": 282,
+      "total_points": 282
+    },
+    "ep37": {
+      "score": 84.864746,
+      "scored_points": 283,
+      "total_points": 283
+    },
+    "ep38": {
+      "score": 87.533476,
+      "scored_points": 286,
+      "total_points": 286
+    },
+    "ep39": {
+      "score": 86.861407,
+      "scored_points": 276,
+      "total_points": 276
+    },
+    "ep4": {
+      "score": 101.581291,
+      "scored_points": 292,
+      "total_points": 292
+    },
+    "ep40": {
+      "score": 82.75667,
+      "scored_points": 305,
+      "total_points": 305
+    },
+    "ep41": {
+      "score": 80.468435,
+      "scored_points": 286,
+      "total_points": 286
+    },
+    "ep42": {
+      "score": 100.257944,
+      "scored_points": 276,
+      "total_points": 276
+    },
+    "ep43": {
+      "score": 90.650397,
+      "scored_points": 270,
+      "total_points": 270
+    },
+    "ep44": {
+      "score": 82.375681,
+      "scored_points": 268,
+      "total_points": 268
+    },
+    "ep45": {
+      "score": 91.643396,
+      "scored_points": 274,
+      "total_points": 274
+    },
+    "ep46": {
+      "score": 74.41267,
+      "scored_points": 264,
+      "total_points": 264
+    },
+    "ep47": {
+      "score": 87.068898,
+      "scored_points": 260,
+      "total_points": 260
+    },
+    "ep48": {
+      "score": 83.635761,
+      "scored_points": 238,
+      "total_points": 238
+    },
+    "ep49": {
+      "score": 89.245707,
+      "scored_points": 246,
+      "total_points": 246
+    },
+    "ep5": {
+      "score": 84.583691,
+      "scored_points": 337,
+      "total_points": 337
+    },
+    "ep50": {
+      "score": 65.718189,
+      "scored_points": 352,
+      "total_points": 352
+    },
+    "ep51": {
+      "score": 84.047885,
+      "scored_points": 292,
+      "total_points": 292
+    },
+    "ep52": {
+      "score": 81.087957,
+      "scored_points": 301,
+      "total_points": 301
+    },
+    "ep53": {
+      "score": 104.002432,
+      "scored_points": 284,
+      "total_points": 284
+    },
+    "ep54": {
+      "score": 98.453033,
+      "scored_points": 279,
+      "total_points": 279
+    },
+    "ep55": {
+      "score": 76.302381,
+      "scored_points": 293,
+      "total_points": 293
+    },
+    "ep56": {
+      "score": 97.036988,
+      "scored_points": 288,
+      "total_points": 288
+    },
+    "ep57": {
+      "score": 83.140093,
+      "scored_points": 333,
+      "total_points": 333
+    },
+    "ep58": {
+      "score": 82.231979,
+      "scored_points": 272,
+      "total_points": 272
+    },
+    "ep59": {
+      "score": 100.386918,
+      "scored_points": 289,
+      "total_points": 289
+    },
+    "ep6": {
+      "score": 78.056289,
+      "scored_points": 254,
+      "total_points": 254
+    },
+    "ep60": {
+      "score": 78.676556,
+      "scored_points": 303,
+      "total_points": 303
+    },
+    "ep61": {
+      "score": 108.671679,
+      "scored_points": 275,
+      "total_points": 275
+    },
+    "ep62": {
+      "score": 93.260802,
+      "scored_points": 277,
+      "total_points": 277
+    },
+    "ep63": {
+      "score": 97.635294,
+      "scored_points": 278,
+      "total_points": 278
+    },
+    "ep64": {
+      "score": 93.758359,
+      "scored_points": 305,
+      "total_points": 305
+    },
+    "ep65": {
+      "score": 106.038346,
+      "scored_points": 309,
+      "total_points": 309
+    },
+    "ep66": {
+      "score": 98.792953,
+      "scored_points": 292,
+      "total_points": 292
+    },
+    "ep67": {
+      "score": 84.51903,
+      "scored_points": 285,
+      "total_points": 285
+    },
+    "ep68": {
+      "score": 93.874001,
+      "scored_points": 284,
+      "total_points": 284
+    },
+    "ep69": {
+      "score": 98.908828,
+      "scored_points": 255,
+      "total_points": 255
+    },
+    "ep7": {
+      "score": 89.834963,
+      "scored_points": 284,
+      "total_points": 284
+    },
+    "ep70": {
+      "score": 80.958281,
+      "scored_points": 285,
+      "total_points": 285
+    },
+    "ep71": {
+      "score": 106.877959,
+      "scored_points": 327,
+      "total_points": 327
+    },
+    "ep72": {
+      "score": 80.027406,
+      "scored_points": 338,
+      "total_points": 338
+    },
+    "ep73": {
+      "score": 94.253971,
+      "scored_points": 346,
+      "total_points": 346
+    },
+    "ep74": {
+      "score": 73.597519,
+      "scored_points": 363,
+      "total_points": 363
+    },
+    "ep75": {
+      "score": 106.982201,
+      "scored_points": 326,
+      "total_points": 326
+    },
+    "ep76": {
+      "score": 100.992822,
+      "scored_points": 328,
+      "total_points": 328
+    },
+    "ep77": {
+      "score": 87.040534,
+      "scored_points": 319,
+      "total_points": 319
+    },
+    "ep78": {
+      "score": 86.257002,
+      "scored_points": 349,
+      "total_points": 349
+    },
+    "ep79": {
+      "score": 80.210512,
+      "scored_points": 331,
+      "total_points": 331
+    },
+    "ep8": {
+      "score": 92.908468,
+      "scored_points": 318,
+      "total_points": 318
+    },
+    "ep80": {
+      "score": 94.248841,
+      "scored_points": 319,
+      "total_points": 319
+    },
+    "ep81": {
+      "score": 93.176061,
+      "scored_points": 307,
+      "total_points": 307
+    },
+    "ep82": {
+      "score": 97.04768,
+      "scored_points": 302,
+      "total_points": 302
+    },
+    "ep83": {
+      "score": 108.40709,
+      "scored_points": 328,
+      "total_points": 328
+    },
+    "ep84": {
+      "score": 90.165784,
+      "scored_points": 304,
+      "total_points": 304
+    },
+    "ep85": {
+      "score": 79.984541,
+      "scored_points": 299,
+      "total_points": 299
+    },
+    "ep86": {
+      "score": 103.039802,
+      "scored_points": 261,
+      "total_points": 261
+    },
+    "ep87": {
+      "score": 103.799795,
+      "scored_points": 258,
+      "total_points": 258
+    },
+    "ep88": {
+      "score": 87.321289,
+      "scored_points": 283,
+      "total_points": 283
+    },
+    "ep89": {
+      "score": 89.804597,
+      "scored_points": 307,
+      "total_points": 307
+    },
+    "ep9": {
+      "score": 91.266832,
+      "scored_points": 263,
+      "total_points": 263
+    },
+    "ep90": {
+      "score": 102.714733,
+      "scored_points": 327,
+      "total_points": 327
+    },
+    "ep91": {
+      "score": 83.262167,
+      "scored_points": 302,
+      "total_points": 302
+    },
+    "ep92": {
+      "score": 99.753066,
+      "scored_points": 279,
+      "total_points": 279
+    },
+    "ep93": {
+      "score": 91.043398,
+      "scored_points": 278,
+      "total_points": 278
+    },
+    "ep94": {
+      "score": 107.888382,
+      "scored_points": 308,
+      "total_points": 308
+    },
+    "ep95": {
+      "score": 93.411284,
+      "scored_points": 295,
+      "total_points": 295
+    },
+    "ep96": {
+      "score": 102.163276,
+      "scored_points": 328,
+      "total_points": 328
+    },
+    "ep97": {
+      "score": 90.055705,
+      "scored_points": 293,
+      "total_points": 293
+    },
+    "ep98": {
+      "score": 93.219958,
+      "scored_points": 258,
+      "total_points": 258
+    },
+    "ep99": {
+      "score": 100.646563,
+      "scored_points": 275,
+      "total_points": 275
+    }
+  },
+  "worst_clusters": [
+    {
+      "anchor": 2581,
+      "size": 23,
+      "variance": 321.268309,
+      "episodes": [
+        "ep17",
+        "ep47",
+        "ep6",
+        "ep65"
+      ]
+    },
+    {
+      "anchor": 5584,
+      "size": 42,
+      "variance": 308.443823,
+      "episodes": [
+        "ep14",
+        "ep17",
+        "ep22",
+        "ep24",
+        "ep25",
+        "ep27",
+        "ep32",
+        "ep91"
+      ]
+    },
+    {
+      "anchor": 9342,
+      "size": 91,
+      "variance": 302.274277,
+      "episodes": [
+        "ep11",
+        "ep29",
+        "ep30",
+        "ep34",
+        "ep35",
+        "ep36",
+        "ep39",
+        "ep4",
+        "ep47",
+        "ep58",
+        "ep61"
+      ]
+    },
+    {
+      "anchor": 2582,
+      "size": 23,
+      "variance": 300.729663,
+      "episodes": [
+        "ep17",
+        "ep47",
+        "ep6",
+        "ep65"
+      ]
+    },
+    {
+      "anchor": 5586,
+      "size": 40,
+      "variance": 295.259832,
+      "episodes": [
+        "ep14",
+        "ep17",
+        "ep22",
+        "ep27",
+        "ep32",
+        "ep91"
+      ]
+    },
+    {
+      "anchor": 24766,
+      "size": 36,
+      "variance": 295.218185,
+      "episodes": [
+        "ep0",
+        "ep22",
+        "ep36",
+        "ep39",
+        "ep58",
+        "ep86",
+        "ep93",
+        "ep98"
+      ]
+    },
+    {
+      "anchor": 5585,
+      "size": 39,
+      "variance": 293.082484,
+      "episodes": [
+        "ep14",
+        "ep17",
+        "ep22",
+        "ep24",
+        "ep27",
+        "ep32",
+        "ep91"
+      ]
+    },
+    {
+      "anchor": 27312,
+      "size": 46,
+      "variance": 288.777087,
+      "episodes": [
+        "ep50",
+        "ep64",
+        "ep94"
+      ]
+    },
+    {
+      "anchor": 8215,
+      "size": 61,
+      "variance": 288.270954,
+      "episodes": [
+        "ep29",
+        "ep3",
+        "ep30",
+        "ep34",
+        "ep35",
+        "ep36",
+        "ep39",
+        "ep40",
+        "ep58",
+        "ep61",
+        "ep73",
+        "ep86"
+      ]
+    },
+    {
+      "anchor": 8210,
+      "size": 85,
+      "variance": 287.235612,
+      "episodes": [
+        "ep13",
+        "ep26",
+        "ep33",
+        "ep35",
+        "ep37",
+        "ep38",
+        "ep39",
+        "ep40",
+        "ep43",
+        "ep46",
+        "ep48",
+        "ep53",
+        "ep57",
+        "ep61",
+        "ep74",
+        "ep85",
+        "ep86",
+        "ep9"
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/std_results/run.log b/std_results/run.log
new file mode 100644
index 0000000000000000000000000000000000000000..130f017c27cc351b98580748c79d9905b807e056
--- /dev/null
+++ b/std_results/run.log
@@ -0,0 +1,431 @@
+
+━━ ISR --d-target 12.0 --lambda-acc 0.002 --baseline 3 --gripper-threshold 2.0 ━━
+  ep0: 270 -> 90 frames (33% kept, 39 gripper-forced)
+  ep1: 278 -> 100 frames (36% kept, 39 gripper-forced)
+  ep10: 241 -> 84 frames (35% kept, 37 gripper-forced)
+  ep11: 253 -> 106 frames (42% kept, 66 gripper-forced)
+  ep12: 304 -> 78 frames (26% kept, 24 gripper-forced)
+  ep13: 282 -> 88 frames (31% kept, 44 gripper-forced)
+  ep14: 272 -> 94 frames (35% kept, 46 gripper-forced)
+  ep15: 257 -> 90 frames (35% kept, 46 gripper-forced)
+  ep16: 255 -> 94 frames (37% kept, 41 gripper-forced)
+  ep17: 304 -> 89 frames (29% kept, 34 gripper-forced)
+  ep18: 317 -> 90 frames (28% kept, 29 gripper-forced)
+  ep19: 314 -> 92 frames (29% kept, 39 gripper-forced)
+  ep2: 300 -> 102 frames (34% kept, 39 gripper-forced)
+  ep20: 297 -> 87 frames (29% kept, 27 gripper-forced)
+  ep21: 281 -> 95 frames (34% kept, 45 gripper-forced)
+  ep22: 255 -> 95 frames (37% kept, 42 gripper-forced)
+  ep23: 278 -> 101 frames (36% kept, 44 gripper-forced)
+  ep24: 251 -> 93 frames (37% kept, 40 gripper-forced)
+  ep25: 266 -> 99 frames (37% kept, 48 gripper-forced)
+  ep26: 238 -> 105 frames (44% kept, 63 gripper-forced)
+  ep27: 262 -> 87 frames (33% kept, 36 gripper-forced)
+  ep28: 308 -> 90 frames (29% kept, 37 gripper-forced)
+  ep29: 270 -> 97 frames (36% kept, 40 gripper-forced)
+  ep3: 291 -> 88 frames (30% kept, 33 gripper-forced)
+  ep30: 275 -> 96 frames (35% kept, 40 gripper-forced)
+  ep31: 278 -> 87 frames (31% kept, 36 gripper-forced)
+  ep32: 273 -> 93 frames (34% kept, 42 gripper-forced)
+  ep33: 291 -> 109 frames (37% kept, 65 gripper-forced)
+  ep34: 266 -> 103 frames (39% kept, 60 gripper-forced)
+  ep35: 290 -> 87 frames (30% kept, 39 gripper-forced)
+  ep36: 282 -> 89 frames (32% kept, 42 gripper-forced)
+  ep37: 283 -> 82 frames (29% kept, 39 gripper-forced)
+  ep38: 286 -> 107 frames (37% kept, 68 gripper-forced)
+  ep39: 276 -> 94 frames (34% kept, 47 gripper-forced)
+  ep4: 292 -> 87 frames (30% kept, 35 gripper-forced)
+  ep40: 305 -> 92 frames (30% kept, 45 gripper-forced)
+  ep41: 286 -> 103 frames (36% kept, 62 gripper-forced)
+  ep42: 276 -> 96 frames (35% kept, 49 gripper-forced)
+  ep43: 270 -> 95 frames (35% kept, 43 gripper-forced)
+  ep44: 268 -> 88 frames (33% kept, 35 gripper-forced)
+  ep45: 274 -> 96 frames (35% kept, 46 gripper-forced)
+  ep46: 264 -> 86 frames (33% kept, 40 gripper-forced)
+  ep47: 260 -> 87 frames (33% kept, 39 gripper-forced)
+  ep48: 238 -> 90 frames (38% kept, 53 gripper-forced)
+  ep49: 246 -> 99 frames (40% kept, 46 gripper-forced)
+  ep5: 337 -> 87 frames (26% kept, 37 gripper-forced)
+  ep50: 352 -> 99 frames (28% kept, 40 gripper-forced)
+  ep51: 292 -> 107 frames (37% kept, 51 gripper-forced)
+  ep52: 301 -> 114 frames (38% kept, 62 gripper-forced)
+  ep53: 284 -> 99 frames (35% kept, 50 gripper-forced)
+  ep54: 279 -> 96 frames (34% kept, 43 gripper-forced)
+  ep55: 293 -> 92 frames (31% kept, 36 gripper-forced)
+  ep56: 288 -> 99 frames (34% kept, 54 gripper-forced)
+  ep57: 333 -> 108 frames (32% kept, 46 gripper-forced)
+  ep58: 272 -> 99 frames (36% kept, 46 gripper-forced)
+  ep59: 289 -> 96 frames (33% kept, 42 gripper-forced)
+  ep6: 254 -> 93 frames (37% kept, 45 gripper-forced)
+  ep60: 303 -> 97 frames (32% kept, 40 gripper-forced)
+  ep61: 275 -> 92 frames (33% kept, 41 gripper-forced)
+  ep62: 277 -> 103 frames (37% kept, 51 gripper-forced)
+  ep63: 278 -> 114 frames (41% kept, 66 gripper-forced)
+  ep64: 305 -> 107 frames (35% kept, 57 gripper-forced)
+  ep65: 309 -> 110 frames (36% kept, 53 gripper-forced)
+  ep66: 292 -> 98 frames (34% kept, 40 gripper-forced)
+  ep67: 285 -> 117 frames (41% kept, 67 gripper-forced)
+  ep68: 284 -> 97 frames (34% kept, 43 gripper-forced)
+  ep69: 255 -> 102 frames (40% kept, 62 gripper-forced)
+  ep7: 284 -> 88 frames (31% kept, 40 gripper-forced)
+  ep70: 285 -> 91 frames (32% kept, 36 gripper-forced)
+  ep71: 327 -> 93 frames (28% kept, 42 gripper-forced)
+  ep72: 338 -> 91 frames (27% kept, 35 gripper-forced)
+  ep73: 346 -> 93 frames (27% kept, 35 gripper-forced)
+  ep74: 363 -> 91 frames (25% kept, 21 gripper-forced)
+  ep75: 326 -> 106 frames (33% kept, 47 gripper-forced)
+  ep76: 328 -> 107 frames (33% kept, 40 gripper-forced)
+  ep77: 319 -> 93 frames (29% kept, 35 gripper-forced)
+  ep78: 349 -> 93 frames (27% kept, 35 gripper-forced)
+  ep79: 331 -> 96 frames (29% kept, 30 gripper-forced)
+  ep8: 318 -> 89 frames (28% kept, 36 gripper-forced)
+  ep80: 319 -> 97 frames (30% kept, 34 gripper-forced)
+  ep81: 307 -> 101 frames (33% kept, 38 gripper-forced)
+  ep82: 302 -> 88 frames (29% kept, 35 gripper-forced)
+  ep83: 328 -> 85 frames (26% kept, 29 gripper-forced)
+  ep84: 304 -> 84 frames (28% kept, 35 gripper-forced)
+  ep85: 299 -> 103 frames (34% kept, 45 gripper-forced)
+  ep86: 261 -> 103 frames (39% kept, 61 gripper-forced)
+  ep87: 258 -> 90 frames (35% kept, 41 gripper-forced)
+  ep88: 283 -> 80 frames (28% kept, 25 gripper-forced)
+  ep89: 307 -> 87 frames (28% kept, 33 gripper-forced)
+  ep9: 263 -> 100 frames (38% kept, 47 gripper-forced)
+  ep90: 327 -> 98 frames (30% kept, 38 gripper-forced)
+  ep91: 302 -> 95 frames (31% kept, 35 gripper-forced)
+  ep92: 279 -> 89 frames (32% kept, 39 gripper-forced)
+  ep93: 278 -> 91 frames (33% kept, 38 gripper-forced)
+  ep94: 308 -> 89 frames (29% kept, 33 gripper-forced)
+  ep95: 295 -> 89 frames (30% kept, 37 gripper-forced)
+  ep96: 328 -> 93 frames (28% kept, 35 gripper-forced)
+  ep97: 293 -> 100 frames (34% kept, 40 gripper-forced)
+  ep98: 258 -> 87 frames (34% kept, 34 gripper-forced)
+  ep99: 275 -> 94 frames (34% kept, 37 gripper-forced)
+wrote /workspace/std_mm_teleop/out/isr/stats.json
+
+━━ score raw --input raw --eps 0.5 ━━
+input=raw eps=0.5
+  ep0: score=86.81147 (270/270 points in clusters)
+  ep1: score=85.446293 (278/278 points in clusters)
+  ep10: score=76.033035 (241/241 points in clusters)
+  ep11: score=94.371065 (253/253 points in clusters)
+  ep12: score=58.410363 (304/304 points in clusters)
+  ep13: score=84.338649 (282/282 points in clusters)
+  ep14: score=92.641715 (272/272 points in clusters)
+  ep15: score=98.101925 (257/257 points in clusters)
+  ep16: score=88.488101 (255/255 points in clusters)
+  ep17: score=88.48014 (304/304 points in clusters)
+  ep18: score=79.093761 (317/317 points in clusters)
+  ep19: score=72.769215 (314/314 points in clusters)
+  ep2: score=103.406071 (300/300 points in clusters)
+  ep20: score=79.103303 (297/297 points in clusters)
+  ep21: score=73.820294 (281/281 points in clusters)
+  ep22: score=81.775026 (255/255 points in clusters)
+  ep23: score=80.356488 (278/278 points in clusters)
+  ep24: score=83.348766 (251/251 points in clusters)
+  ep25: score=95.220908 (266/266 points in clusters)
+  ep26: score=96.067244 (238/238 points in clusters)
+  ep27: score=100.979283 (262/262 points in clusters)
+  ep28: score=89.230335 (308/308 points in clusters)
+  ep29: score=83.641057 (270/270 points in clusters)
+  ep3: score=91.383367 (291/291 points in clusters)
+  ep30: score=86.083411 (275/275 points in clusters)
+  ep31: score=84.795859 (278/278 points in clusters)
+  ep32: score=100.813031 (273/273 points in clusters)
+  ep33: score=85.617808 (291/291 points in clusters)
+  ep34: score=84.7752 (266/266 points in clusters)
+  ep35: score=86.533757 (290/290 points in clusters)
+  ep36: score=99.057732 (282/282 points in clusters)
+  ep37: score=84.864746 (283/283 points in clusters)
+  ep38: score=87.533476 (286/286 points in clusters)
+  ep39: score=86.861407 (276/276 points in clusters)
+  ep4: score=101.581291 (292/292 points in clusters)
+  ep40: score=82.75667 (305/305 points in clusters)
+  ep41: score=80.468435 (286/286 points in clusters)
+  ep42: score=100.257944 (276/276 points in clusters)
+  ep43: score=90.650397 (270/270 points in clusters)
+  ep44: score=82.375681 (268/268 points in clusters)
+  ep45: score=91.643396 (274/274 points in clusters)
+  ep46: score=74.41267 (264/264 points in clusters)
+  ep47: score=87.068898 (260/260 points in clusters)
+  ep48: score=83.635761 (238/238 points in clusters)
+  ep49: score=89.245707 (246/246 points in clusters)
+  ep5: score=84.583691 (337/337 points in clusters)
+  ep50: score=65.718189 (352/352 points in clusters)
+  ep51: score=84.047885 (292/292 points in clusters)
+  ep52: score=81.087957 (301/301 points in clusters)
+  ep53: score=104.002432 (284/284 points in clusters)
+  ep54: score=98.453033 (279/279 points in clusters)
+  ep55: score=76.302381 (293/293 points in clusters)
+  ep56: score=97.036988 (288/288 points in clusters)
+  ep57: score=83.140093 (333/333 points in clusters)
+  ep58: score=82.231979 (272/272 points in clusters)
+  ep59: score=100.386918 (289/289 points in clusters)
+  ep6: score=78.056289 (254/254 points in clusters)
+  ep60: score=78.676556 (303/303 points in clusters)
+  ep61: score=108.671679 (275/275 points in clusters)
+  ep62: score=93.260802 (277/277 points in clusters)
+  ep63: score=97.635294 (278/278 points in clusters)
+  ep64: score=93.758359 (305/305 points in clusters)
+  ep65: score=106.038346 (309/309 points in clusters)
+  ep66: score=98.792953 (292/292 points in clusters)
+  ep67: score=84.51903 (285/285 points in clusters)
+  ep68: score=93.874001 (284/284 points in clusters)
+  ep69: score=98.908828 (255/255 points in clusters)
+  ep7: score=89.834963 (284/284 points in clusters)
+  ep70: score=80.958281 (285/285 points in clusters)
+  ep71: score=106.877959 (327/327 points in clusters)
+  ep72: score=80.027406 (338/338 points in clusters)
+  ep73: score=94.253971 (346/346 points in clusters)
+  ep74: score=73.597519 (363/363 points in clusters)
+  ep75: score=106.982201 (326/326 points in clusters)
+  ep76: score=100.992822 (328/328 points in clusters)
+  ep77: score=87.040534 (319/319 points in clusters)
+  ep78: score=86.257002 (349/349 points in clusters)
+  ep79: score=80.210512 (331/331 points in clusters)
+  ep8: score=92.908468 (318/318 points in clusters)
+  ep80: score=94.248841 (319/319 points in clusters)
+  ep81: score=93.176061 (307/307 points in clusters)
+  ep82: score=97.04768 (302/302 points in clusters)
+  ep83: score=108.40709 (328/328 points in clusters)
+  ep84: score=90.165784 (304/304 points in clusters)
+  ep85: score=79.984541 (299/299 points in clusters)
+  ep86: score=103.039802 (261/261 points in clusters)
+  ep87: score=103.799795 (258/258 points in clusters)
+  ep88: score=87.321289 (283/283 points in clusters)
+  ep89: score=89.804597 (307/307 points in clusters)
+  ep9: score=91.266832 (263/263 points in clusters)
+  ep90: score=102.714733 (327/327 points in clusters)
+  ep91: score=83.262167 (302/302 points in clusters)
+  ep92: score=99.753066 (279/279 points in clusters)
+  ep93: score=91.043398 (278/278 points in clusters)
+  ep94: score=107.888382 (308/308 points in clusters)
+  ep95: score=93.411284 (295/295 points in clusters)
+  ep96: score=102.163276 (328/328 points in clusters)
+  ep97: score=90.055705 (293/293 points in clusters)
+  ep98: score=93.219958 (258/258 points in clusters)
+  ep99: score=100.646563 (275/275 points in clusters)
+  dataset: 89.807142
+wrote /workspace/std_mm_teleop/out/scores_raw.json
+
+━━ score isr --input isr --eps 0.5 ━━
+input=isr eps=0.5
+  ep0: score=95.00202 (90/90 points in clusters)
+  ep1: score=98.542921 (100/100 points in clusters)
+  ep10: score=86.822551 (84/84 points in clusters)
+  ep11: score=109.425817 (106/106 points in clusters)
+  ep12: score=74.25414 (76/78 points in clusters)
+  ep13: score=96.884807 (88/88 points in clusters)
+  ep14: score=94.266214 (94/94 points in clusters)
+  ep15: score=107.531641 (90/90 points in clusters)
+  ep16: score=88.997119 (94/94 points in clusters)
+  ep17: score=96.073974 (89/89 points in clusters)
+  ep18: score=85.056971 (90/90 points in clusters)
+  ep19: score=76.398572 (91/92 points in clusters)
+  ep2: score=97.759605 (102/102 points in clusters)
+  ep20: score=90.003539 (87/87 points in clusters)
+  ep21: score=82.76177 (95/95 points in clusters)
+  ep22: score=86.214835 (95/95 points in clusters)
+  ep23: score=80.652655 (101/101 points in clusters)
+  ep24: score=86.160269 (93/93 points in clusters)
+  ep25: score=97.3509 (99/99 points in clusters)
+  ep26: score=98.255658 (105/105 points in clusters)
+  ep27: score=101.469679 (87/87 points in clusters)
+  ep28: score=90.517707 (88/90 points in clusters)
+  ep29: score=90.704959 (97/97 points in clusters)
+  ep3: score=95.036447 (88/88 points in clusters)
+  ep30: score=87.941692 (96/96 points in clusters)
+  ep31: score=84.305406 (87/87 points in clusters)
+  ep32: score=102.471456 (93/93 points in clusters)
+  ep33: score=100.84569 (107/109 points in clusters)
+  ep34: score=93.637763 (103/103 points in clusters)
+  ep35: score=85.282699 (87/87 points in clusters)
+  ep36: score=103.030825 (89/89 points in clusters)
+  ep37: score=102.886738 (82/82 points in clusters)
+  ep38: score=100.675921 (107/107 points in clusters)
+  ep39: score=95.803346 (94/94 points in clusters)
+  ep4: score=103.048067 (86/87 points in clusters)
+  ep40: score=94.477745 (92/92 points in clusters)
+  ep41: score=87.429431 (103/103 points in clusters)
+  ep42: score=106.57331 (96/96 points in clusters)
+  ep43: score=96.033853 (95/95 points in clusters)
+  ep44: score=91.420635 (88/88 points in clusters)
+  ep45: score=89.344375 (96/96 points in clusters)
+  ep46: score=83.684195 (86/86 points in clusters)
+  ep47: score=88.379073 (87/87 points in clusters)
+  ep48: score=96.227835 (90/90 points in clusters)
+  ep49: score=86.935652 (99/99 points in clusters)
+  ep5: score=84.137088 (86/87 points in clusters)
+  ep50: score=70.985933 (99/99 points in clusters)
+  ep51: score=87.512979 (105/107 points in clusters)
+  ep52: score=89.514193 (114/114 points in clusters)
+  ep53: score=101.537133 (99/99 points in clusters)
+  ep54: score=98.882785 (96/96 points in clusters)
+  ep55: score=83.593586 (91/92 points in clusters)
+  ep56: score=102.347671 (99/99 points in clusters)
+  ep57: score=96.379595 (108/108 points in clusters)
+  ep58: score=87.259524 (99/99 points in clusters)
+  ep59: score=98.66277 (96/96 points in clusters)
+  ep6: score=84.755776 (93/93 points in clusters)
+  ep60: score=91.115015 (97/97 points in clusters)
+  ep61: score=109.233537 (92/92 points in clusters)
+  ep62: score=98.924431 (103/103 points in clusters)
+  ep63: score=95.020739 (114/114 points in clusters)
+  ep64: score=93.329454 (107/107 points in clusters)
+  ep65: score=101.546281 (110/110 points in clusters)
+  ep66: score=102.983509 (98/98 points in clusters)
+  ep67: score=79.559885 (116/117 points in clusters)
+  ep68: score=96.681559 (97/97 points in clusters)
+  ep69: score=98.446122 (102/102 points in clusters)
+  ep7: score=107.264314 (88/88 points in clusters)
+  ep70: score=82.662634 (91/91 points in clusters)
+  ep71: score=112.835864 (93/93 points in clusters)
+  ep72: score=83.324836 (85/91 points in clusters)
+  ep73: score=90.955617 (92/93 points in clusters)
+  ep74: score=89.567266 (87/91 points in clusters)
+  ep75: score=101.71279 (106/106 points in clusters)
+  ep76: score=93.253093 (107/107 points in clusters)
+  ep77: score=83.533969 (93/93 points in clusters)
+  ep78: score=81.473739 (90/93 points in clusters)
+  ep79: score=84.682553 (95/96 points in clusters)
+  ep8: score=101.788392 (89/89 points in clusters)
+  ep80: score=92.940747 (97/97 points in clusters)
+  ep81: score=96.430211 (101/101 points in clusters)
+  ep82: score=99.165432 (86/88 points in clusters)
+  ep83: score=115.130011 (85/85 points in clusters)
+  ep84: score=103.389355 (84/84 points in clusters)
+  ep85: score=92.724798 (103/103 points in clusters)
+  ep86: score=108.47794 (103/103 points in clusters)
+  ep87: score=106.220467 (90/90 points in clusters)
+  ep88: score=90.151166 (79/80 points in clusters)
+  ep89: score=94.817476 (87/87 points in clusters)
+  ep9: score=90.439405 (100/100 points in clusters)
+  ep90: score=102.974245 (95/98 points in clusters)
+  ep91: score=88.066799 (95/95 points in clusters)
+  ep92: score=109.730327 (89/89 points in clusters)
+  ep93: score=95.470476 (91/91 points in clusters)
+  ep94: score=106.047394 (89/89 points in clusters)
+  ep95: score=102.336455 (88/89 points in clusters)
+  ep96: score=95.454522 (93/93 points in clusters)
+  ep97: score=89.351045 (100/100 points in clusters)
+  ep98: score=102.596286 (87/87 points in clusters)
+  ep99: score=96.958528 (94/94 points in clusters)
+  dataset: 94.331717
+wrote /workspace/std_mm_teleop/out/scores_isr.json
+
+━━ bucket  ━━
+  ep0: TRAIN      (score 95.00202 ≤ train threshold 95.01138)
+  ep1: REVIEW     (score 98.542921 between thresholds)
+  ep10: TRAIN      (score 86.822551 ≤ train threshold 95.01138)
+  ep11: QUARANTINE (score 109.425817 ≥ quarantine threshold 106.064701)
+  ep12: TRAIN      (score 74.25414 ≤ train threshold 95.01138)
+  ep13: REVIEW     (score 96.884807 between thresholds)
+  ep14: TRAIN      (score 94.266214 ≤ train threshold 95.01138)
+  ep15: QUARANTINE (score 107.531641 ≥ quarantine threshold 106.064701)
+  ep16: TRAIN      (score 88.997119 ≤ train threshold 95.01138)
+  ep17: REVIEW     (score 96.073974 between thresholds)
+  ep18: TRAIN      (score 85.056971 ≤ train threshold 95.01138)
+  ep19: TRAIN      (score 76.398572 ≤ train threshold 95.01138)
+  ep2: REVIEW     (score 97.759605 between thresholds)
+  ep20: TRAIN      (score 90.003539 ≤ train threshold 95.01138)
+  ep21: TRAIN      (score 82.76177 ≤ train threshold 95.01138)
+  ep22: TRAIN      (score 86.214835 ≤ train threshold 95.01138)
+  ep23: TRAIN      (score 80.652655 ≤ train threshold 95.01138)
+  ep24: TRAIN      (score 86.160269 ≤ train threshold 95.01138)
+  ep25: REVIEW     (score 97.3509 between thresholds)
+  ep26: REVIEW     (score 98.255658 between thresholds)
+  ep27: REVIEW     (score 101.469679 between thresholds)
+  ep28: TRAIN      (score 90.517707 ≤ train threshold 95.01138)
+  ep29: TRAIN      (score 90.704959 ≤ train threshold 95.01138)
+  ep3: REVIEW     (score 95.036447 between thresholds)
+  ep30: TRAIN      (score 87.941692 ≤ train threshold 95.01138)
+  ep31: TRAIN      (score 84.305406 ≤ train threshold 95.01138)
+  ep32: REVIEW     (score 102.471456 between thresholds)
+  ep33: REVIEW     (score 100.84569 between thresholds)
+  ep34: TRAIN      (score 93.637763 ≤ train threshold 95.01138)
+  ep35: TRAIN      (score 85.282699 ≤ train threshold 95.01138)
+  ep36: REVIEW     (score 103.030825 between thresholds)
+  ep37: REVIEW     (score 102.886738 between thresholds)
+  ep38: REVIEW     (score 100.675921 between thresholds)
+  ep39: REVIEW     (score 95.803346 between thresholds)
+  ep4: REVIEW     (score 103.048067 between thresholds)
+  ep40: TRAIN      (score 94.477745 ≤ train threshold 95.01138)
+  ep41: TRAIN      (score 87.429431 ≤ train threshold 95.01138)
+  ep42: QUARANTINE (score 106.57331 ≥ quarantine threshold 106.064701)
+  ep43: REVIEW     (score 96.033853 between thresholds)
+  ep44: TRAIN      (score 91.420635 ≤ train threshold 95.01138)
+  ep45: TRAIN      (score 89.344375 ≤ train threshold 95.01138)
+  ep46: TRAIN      (score 83.684195 ≤ train threshold 95.01138)
+  ep47: TRAIN      (score 88.379073 ≤ train threshold 95.01138)
+  ep48: REVIEW     (score 96.227835 between thresholds)
+  ep49: TRAIN      (score 86.935652 ≤ train threshold 95.01138)
+  ep5: TRAIN      (score 84.137088 ≤ train threshold 95.01138)
+  ep50: TRAIN      (score 70.985933 ≤ train threshold 95.01138)
+  ep51: TRAIN      (score 87.512979 ≤ train threshold 95.01138)
+  ep52: TRAIN      (score 89.514193 ≤ train threshold 95.01138)
+  ep53: REVIEW     (score 101.537133 between thresholds)
+  ep54: REVIEW     (score 98.882785 between thresholds)
+  ep55: TRAIN      (score 83.593586 ≤ train threshold 95.01138)
+  ep56: REVIEW     (score 102.347671 between thresholds)
+  ep57: REVIEW     (score 96.379595 between thresholds)
+  ep58: TRAIN      (score 87.259524 ≤ train threshold 95.01138)
+  ep59: REVIEW     (score 98.66277 between thresholds)
+  ep6: TRAIN      (score 84.755776 ≤ train threshold 95.01138)
+  ep60: TRAIN      (score 91.115015 ≤ train threshold 95.01138)
+  ep61: QUARANTINE (score 109.233537 ≥ quarantine threshold 106.064701)
+  ep62: REVIEW     (score 98.924431 between thresholds)
+  ep63: REVIEW     (score 95.020739 between thresholds)
+  ep64: TRAIN      (score 93.329454 ≤ train threshold 95.01138)
+  ep65: REVIEW     (score 101.546281 between thresholds)
+  ep66: REVIEW     (score 102.983509 between thresholds)
+  ep67: TRAIN      (score 79.559885 ≤ train threshold 95.01138)
+  ep68: REVIEW     (score 96.681559 between thresholds)
+  ep69: REVIEW     (score 98.446122 between thresholds)
+  ep7: QUARANTINE (score 107.264314 ≥ quarantine threshold 106.064701)
+  ep70: TRAIN      (score 82.662634 ≤ train threshold 95.01138)
+  ep71: QUARANTINE (score 112.835864 ≥ quarantine threshold 106.064701)
+  ep72: TRAIN      (score 83.324836 ≤ train threshold 95.01138)
+  ep73: TRAIN      (score 90.955617 ≤ train threshold 95.01138)
+  ep74: TRAIN      (score 89.567266 ≤ train threshold 95.01138)
+  ep75: REVIEW     (score 101.71279 between thresholds)
+  ep76: TRAIN      (score 93.253093 ≤ train threshold 95.01138)
+  ep77: TRAIN      (score 83.533969 ≤ train threshold 95.01138)
+  ep78: TRAIN      (score 81.473739 ≤ train threshold 95.01138)
+  ep79: TRAIN      (score 84.682553 ≤ train threshold 95.01138)
+  ep8: REVIEW     (score 101.788392 between thresholds)
+  ep80: TRAIN      (score 92.940747 ≤ train threshold 95.01138)
+  ep81: REVIEW     (score 96.430211 between thresholds)
+  ep82: REVIEW     (score 99.165432 between thresholds)
+  ep83: QUARANTINE (score 115.130011 ≥ quarantine threshold 106.064701)
+  ep84: REVIEW     (score 103.389355 between thresholds)
+  ep85: TRAIN      (score 92.724798 ≤ train threshold 95.01138)
+  ep86: QUARANTINE (score 108.47794 ≥ quarantine threshold 106.064701)
+  ep87: QUARANTINE (score 106.220467 ≥ quarantine threshold 106.064701)
+  ep88: TRAIN      (score 90.151166 ≤ train threshold 95.01138)
+  ep89: TRAIN      (score 94.817476 ≤ train threshold 95.01138)
+  ep9: TRAIN      (score 90.439405 ≤ train threshold 95.01138)
+  ep90: REVIEW     (score 102.974245 between thresholds)
+  ep91: TRAIN      (score 88.066799 ≤ train threshold 95.01138)
+  ep92: QUARANTINE (score 109.730327 ≥ quarantine threshold 106.064701)
+  ep93: REVIEW     (score 95.470476 between thresholds)
+  ep94: REVIEW     (score 106.047394 between thresholds)
+  ep95: REVIEW     (score 102.336455 between thresholds)
+  ep96: REVIEW     (score 95.454522 between thresholds)
+  ep97: TRAIN      (score 89.351045 ≤ train threshold 95.01138)
+  ep98: REVIEW     (score 102.596286 between thresholds)
+  ep99: REVIEW     (score 96.958528 between thresholds)
+counts: {'train': 50, 'review': 40, 'quarantine': 10}
+wrote /workspace/std_mm_teleop/out/report.json
+
+━━ viz --max-episode-figs 12 ━━
+Traceback (most recent call last):
+  File "/workspace/teleop_std_poc/viz.py", line 243, in <module>
+    ap = argparse.ArgumentParser(description="plots for the teleop standardization pipeline")
+NameError: name 'argparse' is not defined
+Traceback (most recent call last):
+  File "/workspace/teleop_std_poc/run_all.py", line 47, in <module>
+    step("viz", ["viz.py", "--max-episode-figs", str(a.max_episode_figs)])
+  File "/workspace/teleop_std_poc/run_all.py", line 21, in step
+    subprocess.run([sys.executable, str(HERE / args[0]), *args[1:]], check=True)
+  File "/usr/lib/python3.10/subprocess.py", line 526, in run
+    raise CalledProcessError(retcode, process.args,
+subprocess.CalledProcessError: Command '['/workspace/.venv-tsp/bin/python', '/workspace/teleop_std_poc/viz.py', '--max-episode-figs', '12']' returned non-zero exit status 1.
diff --git a/std_results/stats_compare.py b/std_results/stats_compare.py
new file mode 100644
index 0000000000000000000000000000000000000000..ad04fc33bcbc62d55ad78b13fadc2d75b6430347
--- /dev/null
+++ b/std_results/stats_compare.py
@@ -0,0 +1,145 @@
+#!/usr/bin/env python3
+"""Dataset comparison card for two LeRobot v3.0 trees (the abc-teleop-style table).
+
+Every row is measured from the files, not read off a README: format/robot/rate/counts come from
+meta, video shape+codec from ffprobe, and the action convention / gripper encoding rows are
+computed from the parquet columns.
+
+    python stats_compare.py --a NAME=/path/to/ds --b NAME=/path/to/ds [--json out.json]
+"""
+import argparse
+import json
+import subprocess
+from pathlib import Path
+
+import numpy as np
+import pyarrow.parquet as pq
+
+
+def load_meta(root: Path) -> dict:
+    return json.loads((root / "meta" / "info.json").read_text())
+
+
+def read_frames(root: Path) -> dict:
+    files = sorted((root / "data").glob("*/*.parquet"))
+    tabs = [pq.read_table(f) for f in files]
+    cols = {}
+    for name in ("observation.state", "action", "episode_index", "timestamp", "task_index"):
+        vals = [t[name].to_pylist() for t in tabs if name in t.column_names]
+        flat = [x for v in vals for x in v]
+        arr = np.array([x[0] if isinstance(x, list) and len(x) == 1 and name != "observation.state"
+                        and name != "action" else x for x in flat])
+        cols[name] = arr
+    return cols
+
+
+def ffprobe(path: Path) -> dict:
+    out = subprocess.run(
+        ["ffprobe", "-v", "error", "-select_streams", "v:0", "-show_entries",
+         "stream=codec_name,width,height,pix_fmt,avg_frame_rate,nb_frames", "-of", "json", str(path)],
+        capture_output=True, text=True).stdout
+    try:
+        return json.loads(out)["streams"][0]
+    except Exception:
+        return {}
+
+
+def action_convention(state: np.ndarray, action: np.ndarray, ep: np.ndarray) -> dict:
+    """Is action[t] the next state (absolute target), the current state, or a lagging command?"""
+    res = {}
+    for lag in range(0, 7):
+        errs = []
+        for e in np.unique(ep):
+            m = ep == e
+            s, a = state[m], action[m]
+            if len(s) <= lag + 1:
+                continue
+            errs.append(np.abs(a[: len(a) - lag] - s[lag:]).mean())
+        res[lag] = float(np.mean(errs)) if errs else float("nan")
+    best = min(res, key=lambda k: res[k])
+    return {"mae_vs_state_at_lag": {str(k): round(v, 4) for k, v in res.items()},
+            "best_lag_frames": int(best), "best_lag_mae_deg": round(res[best], 4)}
+
+
+def gripper_stats(state: np.ndarray, action: np.ndarray, names: list[str]) -> dict:
+    gi = [i for i, n in enumerate(names) if "gripper" in n]
+    if not gi:
+        return {}
+    g = state[:, gi[0]]
+    ga = action[:, gi[0]]
+    lo, hi = float(g.min()), float(g.max())
+    span = hi - lo if hi > lo else 1.0
+    norm = (g - lo) / span
+    return {"state_range": [round(lo, 3), round(hi, 3)],
+            "action_range": [round(float(ga.min()), 3), round(float(ga.max()), 3)],
+            "looks_normalized_01": bool(lo >= -0.01 and hi <= 1.01),
+            "mean": round(float(g.mean()), 3),
+            "mean_normalized": round(float(norm.mean()), 3),
+            "p05_p50_p95": [round(float(np.percentile(g, p)), 3) for p in (5, 50, 95)],
+            "pct_below_25pct_of_span": round(100 * float((norm < 0.25).mean()), 1),
+            "pct_above_75pct_of_span": round(100 * float((norm > 0.75).mean()), 1)}
+
+
+def describe(name: str, root: Path) -> dict:
+    info = load_meta(root)
+    cols = read_frames(root)
+    state, action, ep = cols["observation.state"], cols["action"], cols["episode_index"]
+    state = np.array(state.tolist(), dtype=np.float64)
+    action = np.array(action.tolist(), dtype=np.float64)
+    cams = [k for k, v in info["features"].items() if v.get("dtype") == "video"]
+    vid = {}
+    for c in cams:
+        f = sorted((root / "videos" / c).glob("*/*.mp4"))
+        if f:
+            p = ffprobe(f[0])
+            vid[c] = {"codec": p.get("codec_name"), "size": f"{p.get('width')}x{p.get('height')}",
+                      "pix_fmt": p.get("pix_fmt"), "fps": p.get("avg_frame_rate"),
+                      "n_files": len(f)}
+    lens = np.array([int((ep == e).sum()) for e in np.unique(ep)])
+    fps = info["fps"]
+    names = info["features"]["action"].get("names") or []
+    # the task string is stored as the pandas index, so the column is "task" or "__index_level_0__"
+    tt = pq.read_table(root / "meta" / "tasks.parquet").to_pydict()
+    tasks = {"task": next(v for k, v in tt.items() if k != "task_index")}
+    return {
+        "name": name, "root": str(root),
+        "format": f"LeRobot {info['codebase_version']}",
+        "robot_type": info.get("robot_type"),
+        "fps": fps,
+        "episodes": int(info["total_episodes"]), "frames": int(info["total_frames"]),
+        "tasks": int(info["total_tasks"]),
+        "task_examples": tasks["task"][:3],
+        "duration_s": round(int(info["total_frames"]) / fps, 1),
+        "duration_hms": f"{int(info['total_frames'])/fps/60:.1f} min",
+        "episode_len": {"mean": round(float(lens.mean()), 1), "min": int(lens.min()),
+                        "max": int(lens.max()), "median": int(np.median(lens))},
+        "camera_keys": cams, "video": vid,
+        "state_dtype": f"{info['features']['observation.state']['dtype']}"
+                       f"[{info['features']['observation.state']['shape'][0]}]",
+        "action_dtype": f"{info['features']['action']['dtype']}[{info['features']['action']['shape'][0]}]",
+        "state_layout": names,
+        "state_range_deg": [[round(float(state[:, i].min()), 1), round(float(state[:, i].max()), 1)]
+                            for i in range(state.shape[1])],
+        "action_convention": action_convention(state, action, ep),
+        "gripper": gripper_stats(state, action, names),
+    }
+
+
+def main():
+    ap = argparse.ArgumentParser()
+    ap.add_argument("--a", required=True, help="NAME=/path")
+    ap.add_argument("--b", required=True, help="NAME=/path")
+    ap.add_argument("--json", default=None)
+    args = ap.parse_args()
+    out = []
+    for spec in (args.a, args.b):
+        n, p = spec.split("=", 1)
+        out.append(describe(n, Path(p)))
+    print(json.dumps(out, indent=2))
+    if args.json:
+        Path(args.json).write_text(json.dumps(out, indent=2))
+        print(f"\nwrote {args.json}")
+
+
+if __name__ == "__main__":
+    main()