Pith Number

pith:L3RKWYWF

pith:2026:L3RKWYWFJQ4BNXY42UHJEXOEWL

not attested not anchored not stored refs pending

HEX: Humanoid-Aligned Experts for Cross-Embodiment Whole-Body Manipulation

Badong Chen, Chengkai Hou, Fei Liao, Jian Tang, Jiawei Wang, Kun Wu, Langzhe Gu, Lei Sun, Meng Li, Shanghang Zhang, Shuanghao Bai, Wanqi Zhou, Xinhua Wang, Xinyuan Lv, Zhengping Che, Zhiyuan Xu, Ziluo Ding

HEX achieves state-of-the-art whole-body manipulation on humanoid robots by aligning states across embodiments and modeling coordination with a mixture-of-experts predictor.

arxiv:2604.07993 v2 · 2026-04-09 · cs.RO

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\pithnumber{L3RKWYWFJQ4BNXY42UHJEXOEWL}

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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

Experiments on real-world humanoid manipulation tasks show that HEX achieves state-of-the-art performance in task success rate and generalization, particularly in fast-reaction and long-horizon scenarios.

C2weakest assumption

That the humanoid-aligned universal state representation combined with the Mixture-of-Experts Unified Proprioceptive Predictor can reliably capture and generalize whole-body coordination and temporal dynamics across heterogeneous embodiments from large-scale multi-embodiment trajectory data.

C3one line summary

HEX is a new framework with humanoid-aligned state representation, mixture-of-experts proprioceptive predictor, history tokens, and residual-gated fusion that achieves state-of-the-art success and generalization on real humanoid manipulation tasks.

Cited by

3 papers in Pith

Any2Any: Efficient Cross-Embodiment Transfer for Humanoid Whole-Body Tracking

RoboMemArena: A Comprehensive and Challenging Robotic Memory Benchmark

CapVector: Learning Transferable Capability Vectors in Parametric Space for Vision-Language-Action Models

Receipt and verification

First computed	2026-05-20T01:05:12.763135Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

5ee2ab62c54c3816df1cd50e925dc4b2f78c24392e01a1ad5b9946f95d355144

Aliases

arxiv: 2604.07993 · arxiv_version: 2604.07993v2 · doi: 10.48550/arxiv.2604.07993 · pith_short_12: L3RKWYWFJQ4B · pith_short_16: L3RKWYWFJQ4BNXY4 · pith_short_8: L3RKWYWF

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/L3RKWYWFJQ4BNXY42UHJEXOEWL \
  | 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: 5ee2ab62c54c3816df1cd50e925dc4b2f78c24392e01a1ad5b9946f95d355144

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "b5712e9fd01eaf7464aaaf029a9f92d2507994a1189a21704c422048ea990fd0",
    "cross_cats_sorted": [],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.RO",
    "submitted_at": "2026-04-09T09:01:43Z",
    "title_canon_sha256": "303deb626acdf6dc44d0b5298cf9fd98db8e61898e8e21073d7204bfb6ab1381"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2604.07993",
    "kind": "arxiv",
    "version": 2
  }
}