Pith Number

pith:O65NSA5T

pith:2026:O65NSA5TMVGGLUXZ364C57JXTZ

not attested not anchored not stored refs pending

AnchorRefine: Synergy-Manipulation Based on Trajectory Anchor and Residual Refinement for Vision-Language-Action Models

Baocai Yin, Chunmian Lin, Daxin Tian, Guixian Qu, Jiapu Wang, Kan Guo, Lanping Qian, Tingzheng Jia, Yongli Hu

AnchorRefine factorizes VLA action prediction into a coarse trajectory anchor plus residual refinement to raise manipulation precision.

arxiv:2604.17787 v2 · 2026-04-20 · 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

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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

Experiments on LIBERO, CALVIN, and real-robot tasks demonstrate that AnchorRefine consistently improves both regression-based and diffusion-based VLA backbones, yielding gains of up to 7.8% in simulation success rate and 18% in real-world success rate.

C2weakest assumption

The assumption that monolithic unified action spaces cause large motions to dominate learning and suppress small corrective signals, and that explicitly factorizing into anchor plus residual will reliably improve geometric and contact precision without new failure modes.

C3one line summary

AnchorRefine factorizes VLA action generation into a trajectory anchor for coarse planning and residual refinement for local corrections, improving success rates by up to 7.8% in simulation and 18% on real robots across LIBERO, CALVIN, and physical tasks.

Cited by

1 paper in Pith

Safe Embodied AI for Long-horizon Tasks: A Cross-layer Analysis of Robotic Manipulation

Receipt and verification

First computed	2026-07-22T01:22:41.373337Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

77bad903b3654c65d2f9dfb82efd379e5668f9ce177ed59701bc5c6fc077b86b

Aliases

arxiv: 2604.17787 · arxiv_version: 2604.17787v2 · doi: 10.48550/arxiv.2604.17787 · pith_short_12: O65NSA5TMVGG · pith_short_16: O65NSA5TMVGGLUXZ · pith_short_8: O65NSA5T

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/O65NSA5TMVGGLUXZ364C57JXTZ \
  | 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: 77bad903b3654c65d2f9dfb82efd379e5668f9ce177ed59701bc5c6fc077b86b

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "17a023e370d94ad2a9fd2c3abea104c4ad14f980fa2e28df751f7b4ddc586185",
    "cross_cats_sorted": [
      "cs.AI"
    ],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.RO",
    "submitted_at": "2026-04-20T04:25:24Z",
    "title_canon_sha256": "7ad7e8a3d3f2c48b317c9d28cc1c8737438b7f635cb5beb9d5960ca96ef77e5a"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2604.17787",
    "kind": "arxiv",
    "version": 2
  }
}