pith:T6SBYLI2
Towards Generalizable Robotic Manipulation in Dynamic Environments
PUMA integrates scene-centric historical optical flow with specialized world queries to implicitly forecast object states, delivering a 6.3% success-rate gain on dynamic manipulation tasks.
arxiv:2603.15620 v3 · 2026-03-16 · cs.CV · cs.RO
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Claims
PUMA achieves state-of-the-art performance, yielding a 6.3% absolute improvement in success rate over baselines by integrating scene-centric historical optical flow and specialized world queries to implicitly forecast object-centric future states.
That scene-centric historical optical flow combined with specialized world queries is sufficient to enable reliable short-horizon prediction of object states in dynamic environments without requiring explicit physics modeling or longer-term forecasting.
DOMINO dataset and PUMA architecture enable better dynamic robotic manipulation by incorporating motion history, delivering 6.3% higher success rates than prior VLA models.
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| First computed | 2026-07-01T01:17:49.087670Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
9fa41c2d1ad7de217d6ce2e24053db999aa6bfd0e092886453291d073b476da4
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curl -sH 'Accept: application/ld+json' https://pith.science/pith/T6SBYLI227PCC7LM4LREAU63TG \
| 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: 9fa41c2d1ad7de217d6ce2e24053db999aa6bfd0e092886453291d073b476da4
Canonical record JSON
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