Pith Number

pith:7TVTQ43S

pith:2026:7TVTQ43SE35GETFQGQMVEZNAHV

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Efficient Emotion-Aware Iconic Gesture Prediction for Robot Co-Speech

Christian Arzate Cruz, Edwin C. Montiel-Vazquez, Giorgos Giannakakis, Randy Gomez, Stefanos Gkikas, Thomas Kassiotis

A lightweight transformer predicts iconic gestures for robots from text and emotion alone, outperforming GPT-4o on the BEAT2 dataset.

arxiv:2604.11417 v4 · 2026-04-13 · cs.RO · cs.AI

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Claims

C1strongest claim

The lightweight transformer outperforms GPT-4o in both semantic gesture placement classification and intensity regression on the BEAT2 dataset, while remaining computationally compact and suitable for real-time deployment on embodied agents.

C2weakest assumption

That text and emotion labels alone are sufficient to accurately predict iconic gestures without audio cues, and that the BEAT2 dataset captures representative real-world co-speech behavior for this task.

C3one line summary

Lightweight transformer predicts iconic gesture placement and intensity from text and emotion for robot co-speech, outperforming GPT-4o on BEAT2 without audio input.

Receipt and verification

First computed	2026-05-20T00:03:11.131473Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

fceb38737226fa624cb034195265a03d7d5b9565dee3a935a35776c41fbef9ef

Aliases

arxiv: 2604.11417 · arxiv_version: 2604.11417v4 · doi: 10.48550/arxiv.2604.11417 · pith_short_12: 7TVTQ43SE35G · pith_short_16: 7TVTQ43SE35GETFQ · pith_short_8: 7TVTQ43S

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/7TVTQ43SE35GETFQGQMVEZNAHV \
  | 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: fceb38737226fa624cb034195265a03d7d5b9565dee3a935a35776c41fbef9ef

Canonical record JSON

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    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.RO",
    "submitted_at": "2026-04-13T13:02:02Z",
    "title_canon_sha256": "01d5a91e32895bda6d5ae992d1cd0dcb5ad5181288ae3d60a3a389127a462214"
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