Pith Number

pith:C47QFD7K

pith:2026:C47QFD7KVCD3TLW3CPFXDJGYGD

not attested not anchored not stored refs resolved

SECOND-Grasp: Semantic Contact-guided Dexterous Grasping

Han Yi Shin, Heeju Ko, Honglak Lee, Jaehyeok Lee, Jaewon Mun, Qixing Huang, Sangpil Kim, Sujin Jang, Sung June Kim

SECOND-Grasp derives dexterous grasp supervision from language-inferred contacts refined across views to reach 98 percent lifting success on seen objects.

arxiv:2605.13117 v1 · 2026-05-13 · cs.RO · cs.AI

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Record completeness

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2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications open

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The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

Our approach, trained on DexGraspNet, consistently outperforms baselines in lifting success rate on both seen and unseen categories, achieving 98.2% and 97.7%, respectively, while also improving intent-aware grasping by 12.8% and 26.2%.

C2weakest assumption

Vision-language reasoning reliably infers appropriate contact regions from object properties and task language, and the Semantic-Geometric Consistency Refinement step produces accurate 3D contact maps without introducing systematic errors that would invalidate downstream inverse-kinematics supervision.

C3one line summary

SECOND-Grasp integrates semantic contact proposals from vision-language reasoning with geometric refinement to achieve 98%+ lifting success and improved intent-aware grasping on seen and unseen objects.

References

59 extracted · 59 resolved · 4 Pith anchors

[1] Qwen3-VL Technical Report 2025 · arXiv:2511.21631

[2] S. Buss. Introduction to inverse kinematics with jacobian transpose, pseudoinverse and damped least squares methods. 2004 2004

[3] B. Calli, A. Singh, A. Walsman, S. Srinivasa, P. Abbeel, and A. M. Dollar. The ycb object and model set: Towards common benchmarks for manipulation research. In2015 international conference on advance 2015

[4] T. Chen, M. Tippur, S. Wu, V . Kumar, E. Adelson, and P. Agrawal. Visual dexterity: In-hand reorientation of novel and complex object shapes.Science Robotics, 8(84):eadc9244, 2023 2023

[5] S. Deng, X. Xu, C. Wu, K. Chen, and K. Jia. 3d affordancenet: A benchmark for visual object affordance understanding. Inproceedings of the IEEE/CVF conference on computer vision and pattern recognitio 2021

Formal links

2 machine-checked theorem links

Receipt and verification

First computed	2026-05-18T03:08:58.034941Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

173f028feaa887b9aedb13cb71a4d830f7deb422adcff5471475ee59e0c6ec60

Aliases

arxiv: 2605.13117 · arxiv_version: 2605.13117v1 · doi: 10.48550/arxiv.2605.13117 · pith_short_12: C47QFD7KVCD3 · pith_short_16: C47QFD7KVCD3TLW3 · pith_short_8: C47QFD7K

Agent API

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/C47QFD7KVCD3TLW3CPFXDJGYGD \
  | 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: 173f028feaa887b9aedb13cb71a4d830f7deb422adcff5471475ee59e0c6ec60

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "7b14c3229a2a4d0b42685810621c4ddfc8d17a7ed244454bf84ef093db207672",
    "cross_cats_sorted": [
      "cs.AI"
    ],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.RO",
    "submitted_at": "2026-05-13T07:37:00Z",
    "title_canon_sha256": "032fe1489f614d10816b631bb84ce1c71ab5cf1bce91251527798ee85836f210"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.13117",
    "kind": "arxiv",
    "version": 1
  }
}