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Paper Citation Record · LEDGER

Scaling Cross-Embodiment World Models for Dexterous Manipulation

As of 23 August 2026, this Paper Citation Record lists 54 of 54 outbound references and 7 inbound Pith citation observations for arXiv:2511.01177.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2511.01177 v3

Coverage vector

measured 54 of 54 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T00:28:29.177561Z

measured 61 of 61 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+00:00

measured 7 of 7 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T01:02:16.311706Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-05-21T12:30:07.629555Z

Reference resolution

54 of 54 outbound references displayed

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  • verified fuzzy0
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External citation measurements

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Outbound references

Observation e0468326-3cae-4647-8afc-0289ab957b7f · outbound

This paper cites A Review of Learning-Based Dynamics Models for Robotic Manipulation.

Scaling Cross-Embodiment World Models for Dexterous Manipulation A Review of Learning-Based Dynamics Models for Robotic Manipulation

Reference 1

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Observation 452db657-552a-4371-8973-e849eb34f7f7 · outbound

This paper cites RoboPack: Learning Tactile-Informed Dynam- ics Models for Dense Packing.

Scaling Cross-Embodiment World Models for Dexterous Manipulation RoboPack: Learning Tactile-Informed Dynam- ics Models for Dense Packing

Reference 2

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source=pdf_text observed=2026-08-04T00:28:22.743896Z digest=sha256:e230ae9330ed9a36fd517113e335cc15a02fd49e21ad62262a779f699314b96f

Observation b775aa14-06c3-426f-9c47-47584179ca34 · outbound

This paper cites Towards Embodiment Scaling Laws in Robot Locomotion.

Scaling Cross-Embodiment World Models for Dexterous Manipulation Towards Embodiment Scaling Laws in Robot Locomotion

Reference 3

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Observation 985df8e9-b88f-48c1-92da-1b816ba702ce · outbound

This paper cites $\pi_0$: A Vision-Language-Action Flow Model for General Robot Control.

Scaling Cross-Embodiment World Models for Dexterous Manipulation $\pi_0$: A Vision-Language-Action Flow Model for General Robot Control

Reference 4

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source=pdf_text observed=2026-08-04T00:28:22.981176Z digest=sha256:0f4a9f2b09e7d9fb5a20d86c645a2aa61af9130655907935c050dd3c60896c17

Observation 23b7fd8a-515b-4693-8e03-b1239b8602e7 · outbound

This paper cites One Policy to Run Them All: an End-to-end Learning Approach to Multi-Embodiment Loco- motion.

Scaling Cross-Embodiment World Models for Dexterous Manipulation One Policy to Run Them All: an End-to-end Learning Approach to Multi-Embodiment Loco- motion

Reference 5

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Observation 04950bfd-3ffe-4434-93b6-c8fc5f92c909 · outbound

This paper cites ViViDex: Learning Vision-based Dexterous Manipulation from Human Videos.

Scaling Cross-Embodiment World Models for Dexterous Manipulation ViViDex: Learning Vision-based Dexterous Manipulation from Human Videos

Reference 6

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Observation 80e0673b-a34d-4be1-908b-e35055298909 · outbound

This paper cites Diffusion policy: Visuomotor policy learning via action diffusion.

Scaling Cross-Embodiment World Models for Dexterous Manipulation Diffusion policy: Visuomotor policy learning via action diffusion

Reference 7

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source=pdf_text observed=2026-08-04T00:28:23.354676Z digest=sha256:a2c60c4ff8fe5db15b50cd93bf6fb09da3a044f2ccb6e92481e4be283eaae612

Observation 5142a9cb-1673-472c-bc9f-181f8d933514 · outbound

This paper cites Learning universal policies via text-guided video generation.

Scaling Cross-Embodiment World Models for Dexterous Manipulation Learning universal policies via text-guided video generation

Reference 8

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source=pdf_text observed=2026-08-04T00:28:23.494068Z digest=sha256:8b8c146c617951e2bdb15f071f9fe009660a5f4c687eb46f15d976120d4c09f6

Observation bd6de63f-c00e-4648-9830-97be9aeb43bc · outbound

This paper cites Visual Foresight: Model-Based Deep Reinforcement Learning for Vision-Based Robotic Control.

Scaling Cross-Embodiment World Models for Dexterous Manipulation Visual Foresight: Model-Based Deep Reinforcement Learning for Vision-Based Robotic Control

Reference 9

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source=pdf_text observed=2026-08-04T00:28:23.667651Z digest=sha256:dc712267679c4dbc7b16c05a286af2b2c970d61c919cb98936c7af8c0ef18493

Observation c9147017-93c2-401b-8e9a-a138c16bd6cc · outbound

This paper cites AnyDexGrasp: General Dexterous Grasping for Different Hands with Human-level Learning Efficiency.

Scaling Cross-Embodiment World Models for Dexterous Manipulation AnyDexGrasp: General Dexterous Grasping for Different Hands with Human-level Learning Efficiency

Reference 10

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source=pdf_text observed=2026-08-04T00:28:23.792133Z digest=sha256:ea64d8b9827a89e6a9e829a03abffc1a3941c451ed4bb156deb336811217d444

Observation a874ed5b-3a81-4999-99a8-34fee172fcfc · outbound

This paper cites RH20T: A Comprehensive Robotic Dataset for Learning Diverse Skills in One-Shot.

Scaling Cross-Embodiment World Models for Dexterous Manipulation RH20T: A Comprehensive Robotic Dataset for Learning Diverse Skills in One-Shot

Reference 11

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Observation 97a44b16-0239-42ea-8659-f5d2f2485d5c · outbound

This paper cites The GRASP Taxonomy of Human Grasp Types.

Scaling Cross-Embodiment World Models for Dexterous Manipulation The GRASP Taxonomy of Human Grasp Types

Reference 12

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source=pdf_text observed=2026-08-04T00:28:24.096100Z digest=sha256:ae5109e7daa99c813b040e93531018c658c3e88b8d9843a03c1e8dc5011a7320

Observation aceb8207-5e38-4020-9378-8e1185cd04c3 · outbound

This paper cites Dextreme: Transfer of agile in-hand manipulation from simulation to reality.

Scaling Cross-Embodiment World Models for Dexterous Manipulation Dextreme: Transfer of agile in-hand manipulation from simulation to reality

Reference 13

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source=pdf_text observed=2026-08-04T00:28:24.201992Z digest=sha256:e7697d49c3e483dc935c0d1970b67d57abbf9637c7f35e75389ace2bd0b984bf

Observation df94639f-e222-47c0-b0e1-69f664efd7d1 · outbound

This paper cites Learning Particle-Based World Model from Human for Robot Dexterous Manipulation.

Scaling Cross-Embodiment World Models for Dexterous Manipulation Learning Particle-Based World Model from Human for Robot Dexterous Manipulation

Reference 14

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source=pdf_text observed=2026-08-04T00:28:24.383201Z digest=sha256:f1e6a045f1fc5dd5d172189c8d93bf0ec0a70549a3ffdc1fbd1418bbbafebc82

Observation a189075c-09ec-4e4e-815f-f552fb765835 · outbound

This paper cites The foundation of efficient robot learning.

Scaling Cross-Embodiment World Models for Dexterous Manipulation The foundation of efficient robot learning

Reference 15

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source=pdf_text observed=2026-08-04T00:28:24.471685Z digest=sha256:ff469cbca47db668762fcd887449851e181f1e4c8b31eb3b2c6909cf5cd73fd6

Observation 2b4a0573-6961-49e3-9ad7-5b202d523f72 · outbound

This paper cites Poisson surface reconstruction.

Scaling Cross-Embodiment World Models for Dexterous Manipulation Poisson surface reconstruction

Reference 16

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Observation 4049e47e-d2c2-48a2-b7a0-cff8cf0f3ac5 · outbound

This paper cites OpenVLA: An Open-Source Vision- Language-Action Model.

Scaling Cross-Embodiment World Models for Dexterous Manipulation OpenVLA: An Open-Source Vision- Language-Action Model

Reference 17

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Observation b2bc7891-6757-46d3-abed-b2e43b8b3dee · outbound

This paper cites DexDeform: Dexterous Deformable Object Manipulation with Human Demonstrations and Differen- tiable Physics.

Scaling Cross-Embodiment World Models for Dexterous Manipulation DexDeform: Dexterous Deformable Object Manipulation with Human Demonstrations and Differen- tiable Physics

Reference 18

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source=pdf_text observed=2026-08-04T00:28:24.776656Z digest=sha256:31baf013e67c9c9f0c30db38fb080f1bab70a45161835162ed1d22db738b8568

Observation e521f1d2-305e-4067-be59-3b6c56901a47 · outbound

This paper cites Learning Particle Dynamics for Manipu- lating Rigid Bodies, Deformable Objects, and Fluids.

Scaling Cross-Embodiment World Models for Dexterous Manipulation Learning Particle Dynamics for Manipu- lating Rigid Bodies, Deformable Objects, and Fluids

Reference 19

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source=pdf_text observed=2026-08-04T00:28:24.901439Z digest=sha256:88b0c7e6ae8f9a097576eb6f63667d12a99dd807ffb3bf3369dc15516c93ec76

Observation c28284d9-378b-4f30-a239-75edb74e9aa5 · outbound

This paper cites Learning Visible Connectivity Dynamics for Cloth Smoothing.

Scaling Cross-Embodiment World Models for Dexterous Manipulation Learning Visible Connectivity Dynamics for Cloth Smoothing

Reference 20

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source=pdf_text observed=2026-08-04T00:28:25.015011Z digest=sha256:0f431f6df6d0b72efae73658e4d8ee7e3d15dd9c7ba83002b7f57b340b69a7f9

Observation 04ea3306-e670-4809-8dd8-911f75b48711 · outbound

This paper cites Contact-invariant optimization for hand manipulation.

Scaling Cross-Embodiment World Models for Dexterous Manipulation Contact-invariant optimization for hand manipulation

Reference 21

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source=pdf_text observed=2026-08-04T00:28:25.099236Z digest=sha256:a73e9518560f9af7ced5fa90774fdffb7acc0a34886265c927bbc3a548549ecd

Observation 9596e75f-3d21-4fb0-bb86-2f0f64231ed9 · outbound

This paper cites Open X-Embodiment: Robotic Learning Datasets and RT-X Models : Open X-Embodiment Collab- oration.

Scaling Cross-Embodiment World Models for Dexterous Manipulation Open X-Embodiment: Robotic Learning Datasets and RT-X Models : Open X-Embodiment Collab- oration

Reference 22

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source=pdf_text observed=2026-08-04T00:28:25.155489Z digest=sha256:23be1389f929713ac5e570580060a9aff3aa1dc1aa7eeb6d799a0086952c46d4

Observation 69a364dc-89d0-420f-856f-1d1d793d64e7 · outbound

This paper cites Octo: An Open-Source Generalist Robot Policy.

Scaling Cross-Embodiment World Models for Dexterous Manipulation Octo: An Open-Source Generalist Robot Policy

Reference 23

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Observation 8977a3be-ccd7-491f-ae76-b561d70b9805 · outbound

This paper cites Global Planning for Contact-Rich Ma- nipulation via Local Smoothing of Quasi-Dynamic Contact Models.

Scaling Cross-Embodiment World Models for Dexterous Manipulation Global Planning for Contact-Rich Ma- nipulation via Local Smoothing of Quasi-Dynamic Contact Models

Reference 24

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source=pdf_text observed=2026-08-04T00:28:25.292646Z digest=sha256:5783538dc285255e246db2439ad4ddd320fa8b3a95c61bce940693d7cecd8558

Observation f2069918-79bf-49ea-b8c6-433a8699f9d3 · outbound

This paper cites GET-Zero: Graph Embod- iment Transformer for Zero-shot Embodiment Generaliza- tion.

Scaling Cross-Embodiment World Models for Dexterous Manipulation GET-Zero: Graph Embod- iment Transformer for Zero-shot Embodiment Generaliza- tion

Reference 25

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source=pdf_text observed=2026-08-04T00:28:25.350368Z digest=sha256:3d1ca1a947b202b40281347ee9885f2918c59041ef396a948c15284feb701529

Observation e410dc22-81b5-424c-9b7e-1ae9237936cf · outbound

This paper cites In-hand object rotation via rapid motor adaptation.

Scaling Cross-Embodiment World Models for Dexterous Manipulation In-hand object rotation via rapid motor adaptation

Reference 26

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source=pdf_text observed=2026-08-04T00:28:25.432411Z digest=sha256:f2c86236d734f3aa50847cbbd66f2e5e9e1a2ef5e6eb90d8bd5c93df6c3a9bb6

Observation 12a3c874-e039-4477-8ddd-d6152a249e74 · outbound

This paper cites DexMV: Imitation Learning for Dexter- ous Manipulation from Human Videos.

Scaling Cross-Embodiment World Models for Dexterous Manipulation DexMV: Imitation Learning for Dexter- ous Manipulation from Human Videos

Reference 27

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source=pdf_text observed=2026-08-04T00:28:25.514602Z digest=sha256:c4b225db7a089a29297a8cece22b602f5a315efbe4cd0e7440e221aa1a8478b8

Observation 59bdfa18-a9d4-4739-8ee4-670113dbe636 · outbound

This paper cites Humanoid Policy ˜ Human Policy.

Scaling Cross-Embodiment World Models for Dexterous Manipulation Humanoid Policy ˜ Human Policy

Reference 28

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source=pdf_text observed=2026-08-04T00:28:25.628453Z digest=sha256:0a383c621cfb1d6c53f4392a90ff8c9726e93eb4413be2aaf4649415153a06d5

Observation 5f3b6340-3fa3-4181-ba41-7d0fafe7c459 · outbound

This paper cites In-hand dexterous manipulation of piecewise- smooth 3-d objects.

Scaling Cross-Embodiment World Models for Dexterous Manipulation In-hand dexterous manipulation of piecewise- smooth 3-d objects

Reference 29

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source=pdf_text observed=2026-08-04T00:28:25.750626Z digest=sha256:39e297da96b5a2898f3830caa3a4c0fc44a610315c6f4ececcceae030e79025d

Observation ed06b745-8e16-41f9-9ed8-de87c0e64094 · outbound

This paper cites Articulated hands: Force control and kinematic issues.

Scaling Cross-Embodiment World Models for Dexterous Manipulation Articulated hands: Force control and kinematic issues

Reference 30

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source=pdf_text observed=2026-08-04T00:28:25.855765Z digest=sha256:23e137c8ac4f78175b76869d94d6269ce5ffa4ed8aa6f313bebdfa0932c6134d

Observation 8b6b615f-56a6-4093-925b-484046c68186 · outbound

This paper cites LEAP Hand: Low-Cost, Efficient, and Anthropomorphic Hand for Robot Learning.

Scaling Cross-Embodiment World Models for Dexterous Manipulation LEAP Hand: Low-Cost, Efficient, and Anthropomorphic Hand for Robot Learning

Reference 31

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source=pdf_text observed=2026-08-04T00:28:25.997581Z digest=sha256:74f9076d1a83878f724006fed639bddd965b5761019c3f684ca2d4a2f3d13923

Observation bbede5dc-a3c1-4cab-afe6-aa926b2514a3 · outbound

This paper cites Bimanual Dexterity for Complex Tasks.

Scaling Cross-Embodiment World Models for Dexterous Manipulation Bimanual Dexterity for Complex Tasks

Reference 32

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source=pdf_text observed=2026-08-04T00:28:26.138670Z digest=sha256:917f3de20d08dd64a3321bfdcf35121ecafb4b8b84b8c6dd91d1d0be28622e59

Observation 17521446-8097-4cf4-8aa1-6e3a1c11699a · outbound

This paper cites RoboCook: Long-Horizon Elasto- Plastic Object Manipulation with Diverse Tools.

Scaling Cross-Embodiment World Models for Dexterous Manipulation RoboCook: Long-Horizon Elasto- Plastic Object Manipulation with Diverse Tools

Reference 33

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source=pdf_text observed=2026-08-04T00:28:26.311040Z digest=sha256:a2b11f66e9a40d3c585006e4aa9f5a028d80ecac01495be4c1455adc98a14d7c

Observation 67b7a400-ff44-4a52-b55a-08a4a5588910 · outbound

This paper cites RoboCraft: Learning to see, simulate, and shape elasto-plastic objects in 3D with graph networks.

Scaling Cross-Embodiment World Models for Dexterous Manipulation RoboCraft: Learning to see, simulate, and shape elasto-plastic objects in 3D with graph networks

Reference 34

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source=pdf_text observed=2026-08-04T00:28:26.411508Z digest=sha256:24b03564af16f0934d5ff7e31ffb24d07942735e30631b75ddf9ace2dbea4b1b

Observation 3253b83a-d8b7-4c5e-a1c4-8c13b7b300aa · outbound

This paper cites RoboCraft: Learning to See, Simulate, and Shape Elasto-Plastic Objects with Graph Networks.

Scaling Cross-Embodiment World Models for Dexterous Manipulation RoboCraft: Learning to See, Simulate, and Shape Elasto-Plastic Objects with Graph Networks

Reference 35

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source=pdf_text observed=2026-08-04T00:28:26.584574Z digest=sha256:2f55708e359fa4c9bf4d3514f15940dd4d7866590bbe9dbb87af119d27ef5d89

Observation 41528094-7d03-439f-b0bb-e0af77f9b70b · outbound

This paper cites Dexterous contact-rich manipulation via the contact trust region.

Scaling Cross-Embodiment World Models for Dexterous Manipulation Dexterous contact-rich manipulation via the contact trust region

Reference 36

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source=pdf_text observed=2026-08-04T00:28:26.716749Z digest=sha256:602941266d979c3376519e9b36528d0890da4d383a7ccec798286d1ed7cf11b4

Observation 222bbbbe-21aa-4408-8f78-a6bbe2602a90 · outbound

This paper cites How to Grow a Mind: Statistics, Structure, and Abstraction.

Scaling Cross-Embodiment World Models for Dexterous Manipulation How to Grow a Mind: Statistics, Structure, and Abstraction

Reference 37

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Observation a0bd74d3-4928-4497-9e1e-063bef7e664b · outbound

This paper cites Diffusion Dynamics Models with Generative State Estimation for Cloth Manipulation.

Scaling Cross-Embodiment World Models for Dexterous Manipulation Diffusion Dynamics Models with Generative State Estimation for Cloth Manipulation

Reference 38

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Observation 4c6ac81d-0905-41b3-a1ec-8f2a1f122c4e · outbound

This paper cites LodeStar: Long-horizon Dexterity via Synthetic Data Augmentation from Human Demonstrations.

Scaling Cross-Embodiment World Models for Dexterous Manipulation LodeStar: Long-horizon Dexterity via Synthetic Data Augmentation from Human Demonstrations

Reference 39

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source=pdf_text observed=2026-08-04T00:28:27.129668Z digest=sha256:256fb274c942acb39e05c1a27eff806efa0b329818f59edaa2319b38b085247a

Observation a0940e5e-5e94-46ed-90d8-40e319fe63da · outbound

This paper cites Unidexgrasp++: Improving dexterous grasping policy learning via geometry-aware curriculum and iterative generalist-specialist learning.

Scaling Cross-Embodiment World Models for Dexterous Manipulation Unidexgrasp++: Improving dexterous grasping policy learning via geometry-aware curriculum and iterative generalist-specialist learning

Reference 40

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Observation 965342b6-c51a-4fa3-84cd-c52589a799c7 · outbound

This paper cites Dynamic-Resolution Model Learning for Object Pile Manipulation.

Scaling Cross-Embodiment World Models for Dexterous Manipulation Dynamic-Resolution Model Learning for Object Pile Manipulation

Reference 41

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source=pdf_text observed=2026-08-04T00:28:27.370883Z digest=sha256:ea29da2bb1325be3bb5a3d9385ab0068761f3966fc4155576b2176dadefa37dd

Observation b9e6e37a-9bae-4161-b979-ff7d74a14ca7 · outbound

This paper cites $\mathcal{D(R,O)}$ Grasp: A Unified Representation of Robot and Object Interaction for Cross-Embodiment Dexterous Grasping.

Scaling Cross-Embodiment World Models for Dexterous Manipulation $\mathcal{D(R,O)}$ Grasp: A Unified Representation of Robot and Object Interaction for Cross-Embodiment Dexterous Grasping

Reference 42

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source=pdf_text observed=2026-08-04T00:28:27.475232Z digest=sha256:b1c9b36b679b821517367f34d35523ce424a1ed7ed8841374703ea672f5e7938

Observation 69ce21eb-ce43-4a44-b138-174629cc542c · outbound

This paper cites CAGE: Causal Attention Enables Data-Efficient Generalizable Robotic Manipulation.

Scaling Cross-Embodiment World Models for Dexterous Manipulation CAGE: Causal Attention Enables Data-Efficient Generalizable Robotic Manipulation

Reference 43

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source=pdf_text observed=2026-08-04T00:28:27.608729Z digest=sha256:0b02b5845c728e50fcfe9367da4f8571701e5e1bce4e2d71019aadb36edbf484

Observation 6af46cf4-8ac5-4e02-a635-87b4e22f4f55 · outbound

This paper cites SAPIEN: A SimulAted Part-based Inter- active ENvironment.

Scaling Cross-Embodiment World Models for Dexterous Manipulation SAPIEN: A SimulAted Part-based Inter- active ENvironment

Reference 44

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source=pdf_text observed=2026-08-04T00:28:27.738937Z digest=sha256:416cb430c0e47bbd8baa3f288054e9dae51a8bc21ae5113b100388883133fc64

Observation f6630530-95c0-43af-bf77-5b6ef73b94f0 · outbound

This paper cites Stabilizing Rein- forcement Learning in Differentiable Multiphysics Simula- tion.

Scaling Cross-Embodiment World Models for Dexterous Manipulation Stabilizing Rein- forcement Learning in Differentiable Multiphysics Simula- tion

Reference 45

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source=pdf_text observed=2026-08-04T00:28:27.864506Z digest=sha256:04a465daed9edcd288010cf6a80fc2f861b55285a336688c580eb1007a7acc29

Observation bfff7cb2-56ea-4e33-b610-32541b0f1085 · outbound

This paper cites Pushing the Limits of Cross- Embodiment Learning for Manipulation and Navigation.

Scaling Cross-Embodiment World Models for Dexterous Manipulation Pushing the Limits of Cross- Embodiment Learning for Manipulation and Navigation

Reference 46

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No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

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Observation d8870863-0ca1-4c8f-a1f4-0545dac10ccc · outbound

This paper cites Multi-view Hand Reconstruction with a Point-Embedded Transformer.

Scaling Cross-Embodiment World Models for Dexterous Manipulation Multi-view Hand Reconstruction with a Point-Embedded Transformer

Reference 47

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source=pdf_text observed=2026-08-04T00:28:28.107199Z digest=sha256:800bb28b8488dfb3d96d65678f57318a4cbcfa081d668fb3bbfc765fc438dc2f

Observation 8e9787b9-57e4-4dfd-81a2-19974afeae12 · outbound

This paper cites Learning interactive real-world simu- lators.

Scaling Cross-Embodiment World Models for Dexterous Manipulation Learning interactive real-world simu- lators

Reference 48

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source=pdf_text observed=2026-08-04T00:28:28.280793Z digest=sha256:ce7b8a765e280cdceb2f9c35afdfafb529f441d944cb5f36262a1864f9e5bf7d

Observation f85aafc8-57c6-4c01-9bd7-9893a48d9ece · outbound

This paper cites EgoVLA: Learning Vision-Language-Action Models from Egocentric Human Videos.

Scaling Cross-Embodiment World Models for Dexterous Manipulation EgoVLA: Learning Vision-Language-Action Models from Egocentric Human Videos

Reference 49

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source=pdf_text observed=2026-08-04T00:28:28.407653Z digest=sha256:a6ff2e3ef91552ef55a8f67542af76d89320d2ac5e9cf6cf36f1805f61ab98ba

Observation cf4b9bfe-29a9-4eaf-8694-fd01218a5abf · outbound

This paper cites DexterityGen: Foundation Controller for Unprecedented Dexterity.

Scaling Cross-Embodiment World Models for Dexterous Manipulation DexterityGen: Foundation Controller for Unprecedented Dexterity

Reference 50

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source=pdf_text observed=2026-08-04T00:28:28.578056Z digest=sha256:4d54e245d97d736f92abddcd7d1daafd81cd17b2d1b779e93a6e5e1edaca5ac7

Observation c5afa080-5cc4-4f67-a9a6-544f1ddce99e · outbound

This paper cites AdaptiGraph: Material-Adaptive Graph-Based Neural Dynamics for Robotic Manipulation.

Scaling Cross-Embodiment World Models for Dexterous Manipulation AdaptiGraph: Material-Adaptive Graph-Based Neural Dynamics for Robotic Manipulation

Reference 51

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source=pdf_text observed=2026-08-04T00:28:28.734432Z digest=sha256:df51e0291ea7ba027a70acbd79974534148ec07fc2fe4934e3e46b9c3556666a

Observation 3004ac21-dbf2-441e-8215-134d21af3539 · outbound

This paper cites Dynamic 3D Gaussian Tracking for Graph-Based Neural Dynamics Modeling.

Scaling Cross-Embodiment World Models for Dexterous Manipulation Dynamic 3D Gaussian Tracking for Graph-Based Neural Dynamics Modeling

Reference 52

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source=pdf_text observed=2026-08-04T00:28:28.876695Z digest=sha256:e170dbf1b787b2a9d0ba441417495d59948248e3ade75db98a1ab16d6446de8d

Observation 91fcf7f2-c832-4322-b84a-0947657d2b0d · outbound

This paper cites Dexdlo: Learning goal-conditioned dexterous policy for dynamic manipulation of deformable linear objects.

Scaling Cross-Embodiment World Models for Dexterous Manipulation Dexdlo: Learning goal-conditioned dexterous policy for dynamic manipulation of deformable linear objects

Reference 53

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source=pdf_text observed=2026-08-04T00:28:29.011733Z digest=sha256:0e4cf8557bda3129e25a49521598452f77d37f98315bb3019a7b56e35770bd3f

Observation 2982437f-f7ce-4d08-87d2-5aa60143cf01 · outbound

This paper cites RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control.

Scaling Cross-Embodiment World Models for Dexterous Manipulation RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control

Reference 54

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source=pdf_text observed=2026-08-04T00:28:29.177561Z digest=sha256:f000b32248543f61368da7c96145e4487e3a9ada1fa60fcea13a96168b7cc5e5

Pith citing papers

Observation 30eab830-68ca-4446-8ad5-cd27c5e86a2a · inbound

One Hand to Rule Them All: Canonical Representations for Unified Dexterous Manipulation cites this paper.

One Hand to Rule Them All: Canonical Representations for Unified Dexterous Manipulation Scaling Cross-Embodiment World Models for Dexterous Manipulation

Reference 11

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No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

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Observation e71e36a2-aee5-450d-93a3-2f5b8ff371f4 · inbound

EgoVerse: An Egocentric Human Dataset for Robot Learning from Around the World cites this paper.

EgoVerse: An Egocentric Human Dataset for Robot Learning from Around the World Scaling Cross-Embodiment World Models for Dexterous Manipulation

Reference 25

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source=pdf_text observed=2026-05-10T17:07:41.489995Z digest=sha256:3610555dcb32258a15be3ad330e401083ddcb86833681baca37ad58545f0dd31

Observation b9824886-c32f-4ae7-a6cd-f72c74928edf · inbound

${\pi}_{0.7}$: a Steerable Generalist Robotic Foundation Model with Emergent Capabilities cites this paper.

${\pi}_{0.7}$: a Steerable Generalist Robotic Foundation Model with Emergent Capabilities Scaling Cross-Embodiment World Models for Dexterous Manipulation

Reference 86

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Observation dd3b06db-9a41-4f4a-962d-9751984ab8d6 · inbound

LACE: Latent Visual Representation for Cross-Embodiment Learning cites this paper.

LACE: Latent Visual Representation for Cross-Embodiment Learning Scaling Cross-Embodiment World Models for Dexterous Manipulation

Reference 50

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Observation 09570df5-9e19-4768-b1d8-505a8be26a6f · inbound

PhysCoRe: Physics-Corrected Residual World Models for Material-Aware Deformable Dynamics cites this paper.

PhysCoRe: Physics-Corrected Residual World Models for Material-Aware Deformable Dynamics Scaling Cross-Embodiment World Models for Dexterous Manipulation

Reference 52

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source=pdf_text observed=2026-08-01T09:46:46.978376Z digest=sha256:46e7d768967b6124cbb7da09c35bbb1e10c507de2e88dcae633eec9d70fa7092

Observation 66976dbf-2194-4d99-b104-adca75388b8f · inbound

Rapid Embodiment Adaptation for Quadrupedal Locomotion cites this paper.

Rapid Embodiment Adaptation for Quadrupedal Locomotion Scaling Cross-Embodiment World Models for Dexterous Manipulation

Reference 12

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source=pdf_text observed=2026-08-06T00:09:48.862287Z digest=sha256:d429eb0f22acc8cab3ff85ba73e01cb2b2854cefa0bd5f7b792c81f6e5a5d024

Observation d5ea3da7-8e91-4d44-be7d-a6f72f20300a · inbound

Rapid Embodiment Adaptation for Quadrupedal Locomotion cites this paper.

Rapid Embodiment Adaptation for Quadrupedal Locomotion Scaling Cross-Embodiment World Models for Dexterous Manipulation

Reference 12

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