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Paper Citation Record · LEDGER

MANGO-Grasp: Mahalanobis Fields over Geometry-Oriented 3D Gaussians for Cross-Embodiment Dexterous Grasping

As of 23 August 2026, this Paper Citation Record lists 30 of 30 outbound references and 0 inbound Pith citation observations for arXiv:2608.02014.

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

pith.paper-citation-record.v1
2608.02014 v1

Coverage vector

measured 30 of 30 reference resolution

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Source: paper_references, paper_reference_links, observed 2026-08-04T16:43:10.902706Z

measured 30 of 30 standing notices

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

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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30 of 30 outbound references displayed

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Outbound references

Observation 7eec55f3-e0a6-49a5-b164-ea1e1a53c413 · outbound

This paper cites Synthesizing diverse and physically stable grasps with arbitrary hand structures using differentiable force closure estimator,.

MANGO-Grasp: Mahalanobis Fields over Geometry-Oriented 3D Gaussians for Cross-Embodiment Dexterous Grasping Synthesizing diverse and physically stable grasps with arbitrary hand structures using differentiable force closure estimator,

Reference 1

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Observation 27f62b35-3a8f-43bf-8e3d-87b5802b2c42 · outbound

This paper cites DexGraspNet: A Large-Scale Robotic Dexterous Grasp Dataset for General Objects Based on Simulation.

MANGO-Grasp: Mahalanobis Fields over Geometry-Oriented 3D Gaussians for Cross-Embodiment Dexterous Grasping DexGraspNet: A Large-Scale Robotic Dexterous Grasp Dataset for General Objects Based on Simulation

Reference 2

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Observation df4133d6-e276-4df7-ae5c-dedccf52563e · outbound

This paper cites Fast-grasp’d: Dexterous multi-finger grasp generation through differentiable simulation,.

MANGO-Grasp: Mahalanobis Fields over Geometry-Oriented 3D Gaussians for Cross-Embodiment Dexterous Grasping Fast-grasp’d: Dexterous multi-finger grasp generation through differentiable simulation,

Reference 3

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Observation 30c08158-ee66-4c44-ba82-683b898c6bbd · outbound

This paper cites Dex- terous grasp transformer,.

MANGO-Grasp: Mahalanobis Fields over Geometry-Oriented 3D Gaussians for Cross-Embodiment Dexterous Grasping Dex- terous grasp transformer,

Reference 4

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source=pdf_text observed=2026-08-04T16:43:10.751096Z digest=sha256:046b5a42781b168aba26a7e331efffa059c02bf1fc48e92ed3f328f353ceaeee

Observation 9703f01c-4e5a-466b-9256-9679a42273cb · outbound

This paper cites DexDiffuser: Generating Dexterous Grasps with Diffusion Models.

MANGO-Grasp: Mahalanobis Fields over Geometry-Oriented 3D Gaussians for Cross-Embodiment Dexterous Grasping DexDiffuser: Generating Dexterous Grasps with Diffusion Models

Reference 5

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source=pdf_text observed=2026-08-04T16:43:10.756547Z digest=sha256:f51c84a3878a07b86f5bebe054d304bdd783d8217e2d67bf15a6979de62a4cc9

Observation f1872db7-d46c-4254-8bea-c2c6a5e6ad5a · outbound

This paper cites DexGrasp Anything: Towards Universal Robotic Dexterous Grasping with Physics Awareness.

MANGO-Grasp: Mahalanobis Fields over Geometry-Oriented 3D Gaussians for Cross-Embodiment Dexterous Grasping DexGrasp Anything: Towards Universal Robotic Dexterous Grasping with Physics Awareness

Reference 6

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source=pdf_text observed=2026-08-04T16:43:10.762876Z digest=sha256:f6e5ca0d4971d4e2985347982a708b8e3484788637b158507a41d8d5c5f18a9a

Observation 790ded1f-425e-468a-a638-c48db69da157 · outbound

This paper cites Unidexgrasp: Universal robotic dexterous grasping via learning diverse proposal generation and goal-conditioned policy,.

MANGO-Grasp: Mahalanobis Fields over Geometry-Oriented 3D Gaussians for Cross-Embodiment Dexterous Grasping Unidexgrasp: Universal robotic dexterous grasping via learning diverse proposal generation and goal-conditioned policy,

Reference 7

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source=pdf_text observed=2026-08-04T16:43:10.768929Z digest=sha256:517dba7ce42a56b7dc5564a6262af30f9912d35bc2a7169415ca002d42007d56

Observation 659fc68f-ab6a-4941-83d2-2341283acfb6 · outbound

This paper cites UniDexGrasp++: Improving Dexterous Grasping Policy Learning via Geometry-aware Curriculum and Iterative Generalist-Specialist Learning.

MANGO-Grasp: Mahalanobis Fields over Geometry-Oriented 3D Gaussians for Cross-Embodiment Dexterous Grasping UniDexGrasp++: Improving Dexterous Grasping Policy Learning via Geometry-aware Curriculum and Iterative Generalist-Specialist Learning

Reference 8

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source=pdf_text observed=2026-08-04T16:43:10.774528Z digest=sha256:580a3e5ebcba2f2d9121f1b3ce72904e186343fb3ed697f3470b6a3b811b84ef

Observation af02c675-edf5-45cc-962c-9579542769dd · outbound

This paper cites Unigrasptransformer: Simplified policy distillation for scalable dexterous robotic grasping,.

MANGO-Grasp: Mahalanobis Fields over Geometry-Oriented 3D Gaussians for Cross-Embodiment Dexterous Grasping Unigrasptransformer: Simplified policy distillation for scalable dexterous robotic grasping,

Reference 9

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source=pdf_text observed=2026-08-04T16:43:10.779812Z digest=sha256:4aa8409d9353b89028e83ed593d47f9bd25636c0e0e7c845213e4b6f07efddaf

Observation c1bc4566-0b43-46e7-acfe-43dd29a640d5 · outbound

This paper cites Machagrasp: Morphology-aware cross-embodiment dexterous hand articulation generation for grasping,.

MANGO-Grasp: Mahalanobis Fields over Geometry-Oriented 3D Gaussians for Cross-Embodiment Dexterous Grasping Machagrasp: Morphology-aware cross-embodiment dexterous hand articulation generation for grasping,

Reference 10

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source=pdf_text observed=2026-08-04T16:43:10.784535Z digest=sha256:7f2d975e29a004087f0c81c8d2ed6bc00ffed22a7ad3a4e8d3db651f5f0d9ebe

Observation 71fd38f3-f97a-468b-a002-0b755433892c · outbound

This paper cites Unigrasp: Learning a unified model to grasp with multifingered robotic hands,.

MANGO-Grasp: Mahalanobis Fields over Geometry-Oriented 3D Gaussians for Cross-Embodiment Dexterous Grasping Unigrasp: Learning a unified model to grasp with multifingered robotic hands,

Reference 11

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source=pdf_text observed=2026-08-04T16:43:10.789835Z digest=sha256:d28b6fe2ca9b3fd590f409ccdba6a5cef796db4e8eab3142fbd96fed243f3ee8

Observation 53646fd6-6a0f-45da-a78c-be3612660268 · outbound

This paper cites Gendexgrasp: Generalizable dexterous grasping,.

MANGO-Grasp: Mahalanobis Fields over Geometry-Oriented 3D Gaussians for Cross-Embodiment Dexterous Grasping Gendexgrasp: Generalizable dexterous grasping,

Reference 12

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source=pdf_text observed=2026-08-04T16:43:10.796038Z digest=sha256:e0112f3260556e1746e67a9831ba7377d57efcbb947fcc0158497ebcb8c5fbde

Observation 04c4aa27-509a-4761-b093-776e0f0f98b9 · outbound

This paper cites Geometry matching for multi-embodiment grasping,.

MANGO-Grasp: Mahalanobis Fields over Geometry-Oriented 3D Gaussians for Cross-Embodiment Dexterous Grasping Geometry matching for multi-embodiment grasping,

Reference 13

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source=pdf_text observed=2026-08-04T16:43:10.801006Z digest=sha256:44963fd90ca3046934a6c8fef5e310a2ea9203db8297d833023932da70f13691

Observation b0edc33a-74a8-4619-b86a-f49f0711b8ff · outbound

This paper cites GeoMatch++: Morphology Conditioned Geometry Matching for Multi-Embodiment Grasping.

MANGO-Grasp: Mahalanobis Fields over Geometry-Oriented 3D Gaussians for Cross-Embodiment Dexterous Grasping GeoMatch++: Morphology Conditioned Geometry Matching for Multi-Embodiment Grasping

Reference 14

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source=pdf_text observed=2026-08-04T16:43:10.807034Z digest=sha256:2eb424d9cfd11b5321ee57f8877e6cabc0efbcd37a7a76f5312d716dd14b283d

Observation 855925c6-8ac1-49a2-b837-a4e38459712b · outbound

This paper cites D(R,O)grasp: A unified representation of robot and object interaction for cross-embodiment dexterous grasping,.

MANGO-Grasp: Mahalanobis Fields over Geometry-Oriented 3D Gaussians for Cross-Embodiment Dexterous Grasping D(R,O)grasp: A unified representation of robot and object interaction for cross-embodiment dexterous grasping,

Reference 15

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source=pdf_text observed=2026-08-04T16:43:10.812814Z digest=sha256:a70d663e1c4282774945197a0c77db3652d60bca72bcd69ab2874982a1b6dd72

Observation 1c6cefb7-9b7d-4d15-8653-9c0f7f7ac667 · outbound

This paper cites T(r,o) grasp: Efficient graph diffusion of robot-object spatial transformation for cross-embodiment dexterous grasping,.

MANGO-Grasp: Mahalanobis Fields over Geometry-Oriented 3D Gaussians for Cross-Embodiment Dexterous Grasping T(r,o) grasp: Efficient graph diffusion of robot-object spatial transformation for cross-embodiment dexterous grasping,

Reference 16

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source=pdf_text observed=2026-08-04T16:43:10.819460Z digest=sha256:8e00a2924ba4bd09986ddc49b06fbeb1cfb8ff0f984db81e594a68cd43483f85

Observation 0fe1e768-5843-4377-a248-bf649ae48aca · outbound

This paper cites 3d gaussian splatting for real-time radiance field rendering.

MANGO-Grasp: Mahalanobis Fields over Geometry-Oriented 3D Gaussians for Cross-Embodiment Dexterous Grasping 3d gaussian splatting for real-time radiance field rendering

Reference 17

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source=pdf_text observed=2026-08-04T16:43:10.828739Z digest=sha256:2ac2c03e03eeb8d8f293a5eed1dda4150be527dddef0f0ba86cfbd7798b76e36

Observation d3bb4064-c3a7-43b0-b71c-ce8ac24c1bcc · outbound

This paper cites Multi- grippergrasp: A dataset for robotic grasping from parallel jaw grippers to dexterous hands,.

MANGO-Grasp: Mahalanobis Fields over Geometry-Oriented 3D Gaussians for Cross-Embodiment Dexterous Grasping Multi- grippergrasp: A dataset for robotic grasping from parallel jaw grippers to dexterous hands,

Reference 18

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source=pdf_text observed=2026-08-04T16:43:10.834894Z digest=sha256:ed1c570cd13eca399bb570d67facb3670040711f772218c9cd1d6dd7dcd5d222

Observation 4592ea29-2db0-4623-b80f-b4d6de5fa9fd · outbound

This paper cites Cedex: Cross-embodiment dexterous grasp generation at scale from human-like contact representations,.

MANGO-Grasp: Mahalanobis Fields over Geometry-Oriented 3D Gaussians for Cross-Embodiment Dexterous Grasping Cedex: Cross-embodiment dexterous grasp generation at scale from human-like contact representations,

Reference 19

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source=pdf_text observed=2026-08-04T16:43:10.840023Z digest=sha256:f7a481d1e5b2f863c1129b437392490632871b369b9a78aa35951e0413b28b05

Observation 477da56a-3d41-405b-b61e-5f6758d632a5 · outbound

This paper cites Unimorphgrasp: Diffusion model with morphology-awareness for cross-embodiment dexterous grasp generation,.

MANGO-Grasp: Mahalanobis Fields over Geometry-Oriented 3D Gaussians for Cross-Embodiment Dexterous Grasping Unimorphgrasp: Diffusion model with morphology-awareness for cross-embodiment dexterous grasp generation,

Reference 20

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source=pdf_text observed=2026-08-04T16:43:10.845037Z digest=sha256:10bdbfdda97288e4c55bf07fcc79d6b1d0ee3bf9e31ccc80cfab66a06b9b45e5

Observation 225c3e39-5ea1-4d92-b1c2-bb93d3424882 · outbound

This paper cites Cross-embodiment dexterous grasping with reinforcement learning,.

MANGO-Grasp: Mahalanobis Fields over Geometry-Oriented 3D Gaussians for Cross-Embodiment Dexterous Grasping Cross-embodiment dexterous grasping with reinforcement learning,

Reference 21

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Observation 86825b58-3d51-450b-a1f8-504834bace56 · outbound

This paper cites Sugar: Surface-aligned gaussian splatting for efficient 3d mesh reconstruction and high-quality mesh rendering,.

MANGO-Grasp: Mahalanobis Fields over Geometry-Oriented 3D Gaussians for Cross-Embodiment Dexterous Grasping Sugar: Surface-aligned gaussian splatting for efficient 3d mesh reconstruction and high-quality mesh rendering,

Reference 22

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Observation 83cd104f-daa1-45da-a3c4-2e57226e5298 · outbound

This paper cites 2d gaussian splatting for geometrically accurate radiance fields,.

MANGO-Grasp: Mahalanobis Fields over Geometry-Oriented 3D Gaussians for Cross-Embodiment Dexterous Grasping 2d gaussian splatting for geometrically accurate radiance fields,

Reference 23

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source=pdf_text observed=2026-08-04T16:43:10.864512Z digest=sha256:ce8878440ccc86eb6121a1c6896df5553fa84ac5c01297b3bb0b7735eee8d7ea

Observation 7e5e3791-893a-49ac-ae67-bbe28ad156c0 · outbound

This paper cites Nerfstudio: A modular framework for neural radiance field development,.

MANGO-Grasp: Mahalanobis Fields over Geometry-Oriented 3D Gaussians for Cross-Embodiment Dexterous Grasping Nerfstudio: A modular framework for neural radiance field development,

Reference 24

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Observation 0a8cefe7-3b6e-4d21-9ba9-53464fa71dd7 · outbound

This paper cites Dynamic graph cnn for learning on point clouds,.

MANGO-Grasp: Mahalanobis Fields over Geometry-Oriented 3D Gaussians for Cross-Embodiment Dexterous Grasping Dynamic graph cnn for learning on point clouds,

Reference 25

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source=pdf_text observed=2026-08-04T16:43:10.881415Z digest=sha256:782db3391d8d46ee059aa916fcfc3dce6bbb61d371919ae1f9d3be20ef9ee313

Observation f8b0036a-0c83-46c7-8894-8ca6040dee5c · outbound

This paper cites On the generalised distance in statistics.

MANGO-Grasp: Mahalanobis Fields over Geometry-Oriented 3D Gaussians for Cross-Embodiment Dexterous Grasping On the generalised distance in statistics

Reference 26

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source=pdf_text observed=2026-08-04T16:43:10.886309Z digest=sha256:01cbee0f5240abbe4dfd24069305810d21a21718d5836fad6c2776616c3d4138

Observation 3dc8a53a-10bb-46f6-a74c-fb1fced97587 · outbound

This paper cites Learning structured output represen- tation using deep conditional generative models,.

MANGO-Grasp: Mahalanobis Fields over Geometry-Oriented 3D Gaussians for Cross-Embodiment Dexterous Grasping Learning structured output represen- tation using deep conditional generative models,

Reference 27

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source=pdf_text observed=2026-08-04T16:43:10.892318Z digest=sha256:b082fb0e47d44aef5f3095968b25cb4f8edde2e6ac2fe3eef76df58a590747ce

Observation bba38001-adcf-4600-bfc3-cb53682fbd7a · outbound

This paper cites Gpu-accelerated robotic simulation for distributed reinforce- ment learning,.

MANGO-Grasp: Mahalanobis Fields over Geometry-Oriented 3D Gaussians for Cross-Embodiment Dexterous Grasping Gpu-accelerated robotic simulation for distributed reinforce- ment learning,

Reference 28

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source=pdf_text observed=2026-08-04T16:43:10.897584Z digest=sha256:f0402dd8e16fc62ba71c647a9d5c1aca22e68ac249962f77c667fe477d6ac778

Observation c7b8bdee-d079-4b9a-baf8-e47f953214bf · outbound

This paper cites FoundationPose: Unified 6d pose estimation and tracking of novel objects,.

MANGO-Grasp: Mahalanobis Fields over Geometry-Oriented 3D Gaussians for Cross-Embodiment Dexterous Grasping FoundationPose: Unified 6d pose estimation and tracking of novel objects,

Reference 29

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source=pdf_text observed=2026-08-04T16:43:10.902706Z digest=sha256:32cc2579d8ee6e3be5371f84b6afbf3cbd832e728111addd166374a202c3c50a

Observation c612df93-65d8-4dc3-bf00-125d9e3baff6 · outbound

This paper cites SuGaR: Surface-Aligned Gaussian Splatting for Efficient 3D Mesh Reconstruction and High-Quality Mesh Rendering.

MANGO-Grasp: Mahalanobis Fields over Geometry-Oriented 3D Gaussians for Cross-Embodiment Dexterous Grasping SuGaR: Surface-Aligned Gaussian Splatting for Efficient 3D Mesh Reconstruction and High-Quality Mesh Rendering

Reference 2023

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