Pith Number

pith:R3IUAPTG

pith:2026:R3IUAPTGAQ7QUMN2CU4QOUIV4S

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AssemblyBench: Physics-Aware Assembly of Complex Industrial Objects

Anoop Cherian, Bernhard Egger, Danrui Li, Jiahao Zhang, Moitreya Chatterjee, Suhas Lohit, Tim K. Marks

AssemblyBench dataset and AssemblyDyno model advance physics-aware assembly planning for industrial objects.

arxiv:2605.12845 v1 · 2026-05-13 · cs.CV · cs.AI

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Claims

C1strongest claim

AssemblyDyno outperforms prior works in both assembly pose estimation and trajectory feasibility, where the latter is evaluated by our physics-based simulations.

C2weakest assumption

The synthetic dataset and physics simulations sufficiently capture the shape complexities and physical constraints of real industrial assembly tasks.

C3one line summary

AssemblyBench dataset and AssemblyDyno transformer model enable physics-aware prediction of assembly sequences and trajectories for complex industrial objects from multimodal instructions and 3D shapes.

References

48 extracted · 48 resolved · 4 Pith anchors

[1] The ikea asm dataset: Understanding people assem- bling furniture through actions, objects and pose 2021

[2] Qi Charles, Hao Su, Mo Kaichun, and Leonidas J 2017

[3] A point set generation network for 3D object reconstruction from a sin- gle image 2017

[4] R. Hadsell, S. Chopra, and Y . LeCun. Dimensionality reduc- tion by learning an invariant mapping. InIEEE Computer Society Conference on Computer Vision and Pattern Recog- nition (CVPR), pages 1735–17 2006

[5] ProMQA-Assembly: Multimodal Procedural QA Dataset on Assembly · arXiv:2509.02949

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Receipt and verification

First computed	2026-05-18T03:09:11.908830Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

8ed1403e66043f0a31ba1539075115e49b4e169a555a97d7f5194f8402d124da

Aliases

arxiv: 2605.12845 · arxiv_version: 2605.12845v1 · doi: 10.48550/arxiv.2605.12845 · pith_short_12: R3IUAPTGAQ7Q · pith_short_16: R3IUAPTGAQ7QUMN2 · pith_short_8: R3IUAPTG

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/R3IUAPTGAQ7QUMN2CU4QOUIV4S \
  | 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: 8ed1403e66043f0a31ba1539075115e49b4e169a555a97d7f5194f8402d124da

Canonical record JSON

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  "metadata": {
    "abstract_canon_sha256": "73dd4d14ae3d01ac795555185351db4c4795ff3c775b8761a59e0693d96e0f5e",
    "cross_cats_sorted": [
      "cs.AI"
    ],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.CV",
    "submitted_at": "2026-05-13T00:44:09Z",
    "title_canon_sha256": "44a7f5f981d0f95b7127ee4198c83b4b214772c9524f9c46a9a3a8fd3f2a109c"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.12845",
    "kind": "arxiv",
    "version": 1
  }
}