File size: 2,691 Bytes
42afafb | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 | #!/usr/bin/env python
"""Agent prior card (ablation factor A): what a coding agent can infer about
the cloth parameter card from the reference video alone, without any fitting.
This emulates the "coding agent writes a world + parameters" arm of the
ablation. The agent sees: reference.mp4 (and nothing else). Its priors:
1. Material: it looks like standard polyester/cotton fabric -> areal density
~0.2-0.4 kg/m^2 -> card density 0.35 (anchored working point).
2. Stiffness: folds look soft, drapes over the table edge smoothly ->
flimsy -> bending 0.02 (rag-like).
3. Air drag: the drop in the video falls fast, no parachute behavior ->
air_damp 1.0 (Blender default).
So the agent card IS the default card here, which is the honest baseline:
"agent guesses from priors, no measurement". The NeuIDO arm (B1) instead
*measures* the parameters from the truth trajectories by closed-loop fitting.
We write the card to JSON so the 2x2 ablation runner picks it up.
Usage:
python agent_card.py --world work/B_07/world --out work/B_07/agent_card.json
"""
import argparse
import json
import os
from pathlib import Path
import numpy as np
def main():
ap = argparse.ArgumentParser()
ap.add_argument("--world", required=True)
ap.add_argument("--out", required=True)
args = ap.parse_args()
world = Path(args.world)
spec = json.loads((world / "spec.json").read_text())
z = np.load(world / "dyn" / "Cloth.npz")
pos = z["pos"]
faces = z["faces"]
# cloth area at frame 0 (agent could estimate this from the video geometry)
v = pos[0].astype(np.float64)[faces.astype(np.int64)]
a = v[:, 1] - v[:, 0]
b = v[:, 2] - v[:, 0]
area = float(0.5 * np.linalg.norm(np.cross(a, b), axis=1).sum())
# drop speed visible in the first frames (agent could read this from video)
dt = 1.0 / float(spec["video"]["fps"])
speed = float(np.linalg.norm((pos[6] - pos[0]) / (6 * dt), axis=1).mean())
# agent priors -> card (fabric recognition + free-fall check vs 9.81)
card = {
"density": 0.35, # "standard fabric" prior, kg/m^2 anchor
"bending": 0.02, # "soft drape" prior
"air_damp": 1.0, # "falls like normal cloth" prior
"tensile": 15.0,
"compress": 15.0,
"shear": 8.0,
"method": "agent_prior",
"notes": {
"cloth_area_m2": round(area, 3),
"early_drop_speed_ms": round(speed, 3),
"gravity_freefall_check": round(speed / (6 * dt) / 9.81, 3),
},
}
Path(args.out).write_text(json.dumps(card, indent=1))
print(json.dumps(card, indent=1))
if __name__ == "__main__":
main()
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