pith:XALDECEO
Novel Algorithms for Smoothly Differentiable and Efficiently Vectorizable Contact Manifold Construction
Analytical signed distance function primitives and a novel manifold routine make collision detection smoothly differentiable and massively vectorizable.
arxiv:2604.17538 v2 · 2026-04-19 · cs.RO
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Claims
a method that can address the collision detection part of the puzzle in a manner that is smoothly differentiable and massively vectorizable. This is achieved via two contributions: i) a highly expressive class of analytical SDF primitives that can efficiently represent complex 3D surfaces, ii) a novel contact manifold generation routine that makes use of this geometry representation.
That the new SDF primitives and manifold routine can be composed with existing contact dynamics and time-integration modules without reintroducing non-differentiability or prohibitive computational cost.
New analytical SDF primitives and contact manifold generation enable smoothly differentiable and vectorizable collision detection for robot dynamics.
Receipt and verification
| First computed | 2026-05-26T01:03:30.540561Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
b81632088eb63a6f145357c586857346384dcf910812aee885eb5f6bfbe6ecf3
Aliases
· · · · ·Agent API
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curl -sH 'Accept: application/ld+json' https://pith.science/pith/XALDECEOWY5G6FCTK7CYNBLTIY \
| 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())"
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Canonical record JSON
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