Pith Number

pith:APTM2TIU

pith:2026:APTM2TIUEJM44TDITF64GEW2JP

not attested not anchored not stored refs resolved

CUBic: Coordinated Unified Bimanual Perception and Control Framework

Donglin Wang, Jingkai Xu, Pengxiang Ding, Xingyu Wang, Zhaoxin Fan

CUBic unifies bimanual robot perception and control in a shared tokenized representation where independence and coordination arise from structure alone.

arxiv:2605.13452 v1 · 2026-05-13 · cs.RO · cs.AI

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\pithnumber{APTM2TIUEJM44TDITF64GEW2JP}

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Record completeness

1 Bitcoin timestamp

2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications open

✓ Portable graph bundle live · download bundle · merged state

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

CUBic consistently surpasses standard baselines, achieving marked improvements in coordination accuracy and task success rates over state-of-the-art visuomotor baselines on the RoboTwin benchmark.

C2weakest assumption

That a shared tokenized representation learned through unidirectional aggregation and bidirectional codebook coordination will allow independence and coordination to emerge intrinsically from structure without requiring hand-crafted coupling mechanisms.

C3one line summary

CUBic learns a shared tokenized representation for bimanual robot perception and control via unidirectional aggregation, bidirectional codebook coordination, and a unified diffusion policy, yielding higher coordination accuracy and task success on the RoboTwin benchmark.

References

62 extracted · 62 resolved · 7 Pith anchors

[1] arXiv preprint arXiv:2507.23523 (2025) 3, 10 2025

[2] Kevin Black, Noah Brown, Danny Driess, Adnan Esmail, Michael Equi, Chelsea Finn, Niccolo Fusai, Lachy Groom, Karol Hausman, Brian Ichter, Szymon Jakubczak, Tim Jones, Liyiming Ke, Sergey Levine, Adria 2024

[3] UniVLA: Learning to Act Anywhere with Task-centric Latent Actions 2025 · arXiv:2505.06111

[4] villa-X: Enhancing Latent Action Modeling in Vision-Language-Action Models 2025 · arXiv:2507.23682

[5] Moto: Latent mo- tion token as the bridging language for learning robot ma- nipulation from videos 2025

Formal links

2 machine-checked theorem links

Receipt and verification

First computed	2026-05-18T02:44:41.868799Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

03e6cd4d142259ce4c68997dc312da4bfc3bb16cf9526e2e7a8cdf1db8acb448

Aliases

arxiv: 2605.13452 · arxiv_version: 2605.13452v1 · doi: 10.48550/arxiv.2605.13452 · pith_short_12: APTM2TIUEJM4 · pith_short_16: APTM2TIUEJM44TDI · pith_short_8: APTM2TIU

Agent API

Resolver JSON Graph JSON Events JSON Schema Signing key

Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/APTM2TIUEJM44TDITF64GEW2JP \
  | 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: 03e6cd4d142259ce4c68997dc312da4bfc3bb16cf9526e2e7a8cdf1db8acb448

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "17714d0f6300fe645eeac916c0480d5afbe4f0d196d18ab0375012e1ce98befe",
    "cross_cats_sorted": [
      "cs.AI"
    ],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.RO",
    "submitted_at": "2026-05-13T12:48:23Z",
    "title_canon_sha256": "18c73b5d380a6b6dad853765014dc4256238833d92b28fcaa868182ef0229883"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.13452",
    "kind": "arxiv",
    "version": 1
  }
}