pith:FLL3MXXN
Robotic Control via Embodied Chain-of-Thought Reasoning
Embodied chain-of-thought reasoning trains VLAs to output grounded plans and visuals before actions, raising OpenVLA success by 28 percent.
arxiv:2407.08693 v3 · 2024-07-11 · cs.RO · cs.LG
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Claims
ECoT increases the absolute success rate of OpenVLA, the current strongest open-source VLA policy, by 28% across challenging generalization tasks, without any additional robot training data.
The synthetic data generation pipeline produces reasoning traces that are both accurate enough to supervise the model and sufficiently diverse to improve generalization rather than overfitting to the generation heuristics.
Training VLAs to perform embodied chain-of-thought reasoning about plans, sub-tasks, motions, and grounded visual features before acting raises OpenVLA success rates by 28% on challenging generalization tasks without new robot data.
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| First computed | 2026-05-17T23:38:53.051031Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
2ad7b65eed6806a29828aad57a07b48081ced4cee339a187b7367e5d52c967da
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Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/FLL3MXXNNADKFGBIVLKXUB5UQC \
| 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: 2ad7b65eed6806a29828aad57a07b48081ced4cee339a187b7367e5d52c967da
Canonical record JSON
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