Datasets:

AIGeeksGroup
/

PhysicalWM

#!/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()

	#!/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()