Pith Number

pith:UJZQGCVY

pith:2024:UJZQGCVY76UO2YERCCMIJ5KDJZ

not attested not anchored not stored refs resolved

RoboMIND: Benchmark on Multi-embodiment Intelligence Normative Data for Robot Manipulation

Chengkai Hou, Chenxuan Li, Chenyang Gu, Di Wu, Fei Liao, Guang Yang, Guangyu Li, Jiaming Liu, Jian Tang, Jilei Mao, Jingyang He, Kun Wu, Lecheng Wang, Meng Li, Mengzhen Liu, Ning Liu, Pei Ren, Pengju An, Qiang Zhang, Shanghang Zhang, Shichao Fan, Sixiang Chen, Siyuan Qian, Xiaozhu Ju, Xingyu Wang, Xinhua Wang, Yankai Fu, Yaoxu Lyu, Yinuo Zhao, Yulin Luo, Zeyu Gao, Zhao Jin, Zhengping Che, Zhenyu Wang, Zhen Zhao, Zhiyuan Xu, Zhuqin Yang

RoboMIND supplies 107k teleoperated trajectories across four robot embodiments to train generalizable manipulation policies.

arxiv:2412.13877 v3 · 2024-12-18 · cs.RO · cs.AI

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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

To the best of our knowledge, RoboMIND is the largest multi-embodiment teleoperation dataset collected on a unified platform, providing large-scale and high-quality robotic training data.

C2weakest assumption

That demonstrations collected via human teleoperation on a single unified platform, together with the recorded failure cases, are sufficient in quality and coverage to train policies that generalize across embodiments and to unseen real-world conditions.

C3one line summary

RoboMIND is a large-scale multi-embodiment teleoperation dataset for robot manipulation containing 107k trajectories across four robots, with failure annotations and a digital twin simulator.

References

118 extracted · 118 resolved · 9 Pith anchors

[1] GPT-4 Technical Report 2023 · arXiv:2303.08774

[2] Do as i can, not as i say: Grounding language in robotic affordances 2023

[3] Learning dexterous in-hand manipula- tion 2020

[4] OpenFlamingo: An Open-Source Framework for Training Large Autoregressive Vision-Language Models 2023 · arXiv:2308.01390

[5] Affordances from human videos as a versatile representation for robotics 2023

Formal links

2 machine-checked theorem links

Cited by

29 papers in Pith

PointACT: Vision-Language-Action Models with Multi-Scale Point-Action Interaction

AT-VLA: Adaptive Tactile Injection for Enhanced Feedback Reaction in Vision-Language-Action Models

AnyPos: Automated Task-Agnostic Actions for Bimanual Manipulation

Towards a Multi-Embodied Grasping Agent

RoboCOIN: An Open-Sourced Bimanual Robotic Data Collection for Integrated Manipulation

Receipt and verification

First computed	2026-05-17T23:38:49.979445Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

a273030ab8ffa8ed6091109884f5434e4b456ce207197e0eda640336fee9c895

Aliases

arxiv: 2412.13877 · arxiv_version: 2412.13877v3 · doi: 10.48550/arxiv.2412.13877 · pith_short_12: UJZQGCVY76UO · pith_short_16: UJZQGCVY76UO2YER · pith_short_8: UJZQGCVY

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/UJZQGCVY76UO2YERCCMIJ5KDJZ \
  | 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: a273030ab8ffa8ed6091109884f5434e4b456ce207197e0eda640336fee9c895

Canonical record JSON

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      "cs.AI"
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    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.RO",
    "submitted_at": "2024-12-18T14:17:16Z",
    "title_canon_sha256": "e41524d351c8f6fb1edfb3e3cc66934c619045f0693e88813b6e2421fe6ccfe0"
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