pith:3T4DM6TX
Reactive Motion Generation via Phase-varying Neural Potential Functions
PNPF learns phase-conditioned neural potential functions from demonstrations to produce stable, reactive vector fields that handle state revisits in point-to-point, periodic, and 6D robotic tasks.
arxiv:2604.26450 v1 · 2026-04-29 · cs.RO
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\pithnumber{3T4DM6TX3TVLODJ2Z5HZLK2IU5}
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Record completeness
Claims
PNPF generalizes effectively across point-to-point, periodic, and full 6D motion tasks, outperforms existing baselines on trajectories with intersections, and demonstrates robust performance in real-time robotic manipulation under external disturbances.
That a phase variable can be reliably estimated directly from state progression in a way that disambiguates motion direction at intersections without introducing sensitivity to disturbances or failing on near-identical state pairs.
PNPF learns phase-conditioned neural potential functions from demonstrations to produce stable, reactive vector fields that handle state revisits in point-to-point, periodic, and 6D robotic tasks.
Receipt and verification
| First computed | 2026-05-20T00:05:45.483757Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
dcf8367a77dceab70d3acf4f95ab48a766143fa0c3f4d410069ccdcf6c5878ef
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/3T4DM6TX3TVLODJ2Z5HZLK2IU5 \
| 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: dcf8367a77dceab70d3acf4f95ab48a766143fa0c3f4d410069ccdcf6c5878ef
Canonical record JSON
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