pith:DEP2WS5Q
Robots that learn to evaluate models of collective behavior
A robotic fish distinguishes neural network models of fish behavior as more accurate than rule-based models through closed-loop interactions.
arxiv:2604.07303 v2 · 2026-04-08 · cs.RO
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\pithnumber{DEP2WS5QZP5J5BCIYIBNQY22QI}
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Record completeness
Claims
The neural network-based fish model exhibited the smallest gap across goal-reaching performance and most other metrics, indicating higher behavioral fidelity than conventional rule-based models under this benchmark. More importantly, this separation shows that the proposed evaluation can quantitatively distinguish candidate models under matched closed-loop conditions.
That policies trained in simulation to guide a virtual fish to goals transfer sufficiently well to the physical RoboFish hardware and that the chosen behavioral metrics (goal-reaching, inter-individual distances, wall interactions, alignment) are sufficient to reveal meaningful differences in model fidelity.
A robotic fish uses RL policies to interact with live fish and ranks behavioral models by Wasserstein distance between simulated and real distributions of metrics such as goal-reaching performance and alignment.
Receipt and verification
| First computed | 2026-05-20T02:05:43.090292Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
191fab4bb0cbfa9e8448c202d8635a823bfee45d5626aae23849ae4cf18880a7
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/DEP2WS5QZP5J5BCIYIBNQY22QI \
| jq -c '.canonical_record' \
| python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 191fab4bb0cbfa9e8448c202d8635a823bfee45d5626aae23849ae4cf18880a7
Canonical record JSON
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