Pith Number

pith:3BQG7JNY

pith:2025:3BQG7JNYYOPASRBS4IQZP4RSQE

not attested not anchored not stored refs resolved

EgoDex: Learning Dexterous Manipulation from Large-Scale Egocentric Video

David J. Yoon, Jian Zhang, Mouli Sivapurapu, Peide Huang, Ryan Hoque

EgoDex supplies 829 hours of egocentric video with native 3D hand and finger tracking to train imitation learning policies for dexterous manipulation.

arxiv:2505.11709 v3 · 2025-05-16 · cs.CV · cs.LG · cs.RO

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1 Bitcoin timestamp

2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications open

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Claims

C1strongest claim

EgoDex is the largest and most diverse dataset of dexterous human manipulation to date with 829 hours of egocentric video with paired 3D hand and finger tracking data collected at the time of recording.

C2weakest assumption

That the on-device SLAM and multi-camera tracking from Apple Vision Pro produces sufficiently accurate and unbiased 3D hand poses that can be used to train policies which generalize beyond the collected tabletop tasks to real robotic hardware.

C3one line summary

EgoDex delivers the largest egocentric dataset with native 3D hand tracking for dexterous manipulation, enabling imitation learning policies for hand trajectory prediction on 194 tasks.

References

16 extracted · 16 resolved · 3 Pith anchors

[1] Maple: Encoding dexterous robotic manipulation priors learned from egocentric videos

[2] Kristen Grauman, Andrew Westbury, Eugene Byrne, Zachary Chavis, Antonino Furnari, Rohit Gird- har, Jackson Hamburger, Hao Jiang, Miao Liu, Xingyu Liu, Miguel Martin, Tushar Nagarajan, Ilija Radosavovi 2026

[3] X-il: Exploring the design space of imitation learning policies

[4] Scaling robot supervision to hundreds of hours with robo- turk: Robotic manipulation dataset through human reasoning and dexterity 2026

[5] Ar- mada: Augmented reality for robot manipulation and robot-free data acquisition.arXiv preprint arXiv:2412.10631,

Formal links

2 machine-checked theorem links

Cited by

34 papers in Pith

SFHand: Learning Embodied Manipulation by Streaming Egocentric 3D Hand Forecasting

HoMMI: Learning Whole-Body Mobile Manipulation from Human Demonstrations

MobileEgo Anywhere: Open Infrastructure for long horizon egocentric data on commodity hardware

StableHand: Quality-Aware Flow Matching for World-Space Dual-Hand Motion Estimation from Egocentric Video

LACE: Latent Visual Representation for Cross-Embodiment Learning

Receipt and verification

First computed	2026-05-17T23:38:51.028822Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

d8606fa5b8c39e094432e22197f232811660225bb504a34ec0326c9d364a5fc5

Aliases

arxiv: 2505.11709 · arxiv_version: 2505.11709v3 · doi: 10.48550/arxiv.2505.11709 · pith_short_12: 3BQG7JNYYOPA · pith_short_16: 3BQG7JNYYOPASRBS · pith_short_8: 3BQG7JNY

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/3BQG7JNYYOPASRBS4IQZP4RSQE \
  | 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: d8606fa5b8c39e094432e22197f232811660225bb504a34ec0326c9d364a5fc5

Canonical record JSON

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    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.CV",
    "submitted_at": "2025-05-16T21:34:47Z",
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