Paper Citation Record · LEDGER

GraspIT: A Dataset Bridging the Sim-to-Real gap and back for Validated Grasping SE(3) Pose Generation

As of 13 August 2026, this Paper Citation Record lists 42 of 42 outbound references and 0 inbound Pith citation observations for arXiv:2607.05869.

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

pith.paper-citation-record.v1
2607.05869 v1

Coverage vector

measured 42 of 42 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-08T22:07:01.652521Z

measured 42 of 42 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

42 of 42 outbound references displayed

verified exact7
verified fuzzy32
unresolved2
parse uncertain0
malformed identifier0
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 793f223e-82ec-42e0-9426-1615d35494b5 · outbound

This paper cites an unresolved cited work.

GraspIT: A Dataset Bridging the Sim-to-Real gap and back for Validated Grasping SE(3) Pose Generation Unresolved cited work

Reference 1

Resolution: unresolved; raw_fallback, observed 2026-07-08T22:15:39.837736Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 973752f0-64b2-40b7-964e-13798869e4d2 · outbound

This paper cites RT- 1: Robotics transformer for real-world control at scale.

GraspIT: A Dataset Bridging the Sim-to-Real gap and back for Validated Grasping SE(3) Pose Generation RT- 1: Robotics transformer for real-world control at scale

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T22:15:39.841607Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation c24c1f27-8e63-4bf2-8dff-4b3dfba4bde8 · outbound

This paper cites RT- 2: Vision-language-action models transfer web knowledge to robotic control, 2023.

GraspIT: A Dataset Bridging the Sim-to-Real gap and back for Validated Grasping SE(3) Pose Generation RT- 2: Vision-language-action models transfer web knowledge to robotic control, 2023

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T22:15:39.828825Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation cd821fb6-df37-4a77-8cdc-935caea6e93c · outbound

This paper cites ShapeNet: An Information-Rich 3D Model Repository.

GraspIT: A Dataset Bridging the Sim-to-Real gap and back for Validated Grasping SE(3) Pose Generation ShapeNet: An Information-Rich 3D Model Repository

Reference 4

Resolution: verified exact; local_arxiv, observed 2026-07-08T22:15:39.349539Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 293a76e1-58fd-44fa-92b2-5df1433530f3 · outbound

This paper cites Diffu- sion policy: Visuomotor policy learning via action diffusion.

GraspIT: A Dataset Bridging the Sim-to-Real gap and back for Validated Grasping SE(3) Pose Generation Diffu- sion policy: Visuomotor policy learning via action diffusion

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T22:15:39.836092Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 5e0ced80-8526-4877-8f03-400b767cd75a · outbound

This paper cites Strobl, Matthias Humt, and Rudolph Triebel.

GraspIT: A Dataset Bridging the Sim-to-Real gap and back for Validated Grasping SE(3) Pose Generation Strobl, Matthias Humt, and Rudolph Triebel

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T22:15:39.837906Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation e9a93795-3b80-4fe7-9984-1633ed15f2e6 · outbound

This paper cites Jacquard: A large scale dataset for robotic grasp detection,.

GraspIT: A Dataset Bridging the Sim-to-Real gap and back for Validated Grasping SE(3) Pose Generation Jacquard: A large scale dataset for robotic grasp detection,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T22:15:39.842017Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 880472d3-cc8f-4b56-974d-db6275110154 · outbound

This paper cites Ren, Homer Walke, Quan Vuong, Lucy Xiaoyang Shi, and Sergey Levine.

GraspIT: A Dataset Bridging the Sim-to-Real gap and back for Validated Grasping SE(3) Pose Generation Ren, Homer Walke, Quan Vuong, Lucy Xiaoyang Shi, and Sergey Levine

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T22:15:39.835913Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 0a3dad10-676d-4da9-8732-db6770c84d6a · outbound

This paper cites ACRONYM: A large-scale grasp dataset based on simula- tion.

GraspIT: A Dataset Bridging the Sim-to-Real gap and back for Validated Grasping SE(3) Pose Generation ACRONYM: A large-scale grasp dataset based on simula- tion

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T22:15:39.845761Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 877b7dc7-1d3c-4904-b7ff-e728a551cc39 · outbound

This paper cites an unresolved cited work.

GraspIT: A Dataset Bridging the Sim-to-Real gap and back for Validated Grasping SE(3) Pose Generation Unresolved cited work

Reference 10

Resolution: unresolved; raw_fallback, observed 2026-07-08T22:15:39.806292Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 981c6482-f084-4774-bfb8-a8ac6424b63d · outbound

This paper cites Graspnet-1billion: A large-scale benchmark for general ob- ject grasping.

GraspIT: A Dataset Bridging the Sim-to-Real gap and back for Validated Grasping SE(3) Pose Generation Graspnet-1billion: A large-scale benchmark for general ob- ject grasping

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T22:15:39.815358Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 66bedf68-a8d9-4986-8edd-078a90e37154 · outbound

This paper cites ManiSkill2: A unified benchmark for generalizable manipulation skills,.

GraspIT: A Dataset Bridging the Sim-to-Real gap and back for Validated Grasping SE(3) Pose Generation ManiSkill2: A unified benchmark for generalizable manipulation skills,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T22:15:39.847976Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation adb99be5-7664-43db-9201-97bd5d92d75a · outbound

This paper cites Model based training, detection and pose estimation of texture-less 3d objects in heavily cluttered scenes.

GraspIT: A Dataset Bridging the Sim-to-Real gap and back for Validated Grasping SE(3) Pose Generation Model based training, detection and pose estimation of texture-less 3d objects in heavily cluttered scenes

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T22:15:39.813285Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 20ff5858-35bd-4bc5-aa10-260ae1e1f7d0 · outbound

This paper cites T-LESS: An RGB-D Dataset for 6D Pose Estimation of Texture-less Objects.

GraspIT: A Dataset Bridging the Sim-to-Real gap and back for Validated Grasping SE(3) Pose Generation T-LESS: An RGB-D Dataset for 6D Pose Estimation of Texture-less Objects

Reference 14

Resolution: verified exact; local_arxiv, observed 2026-07-08T22:15:39.350489Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 07abd1ca-7ab1-4611-9305-fc9672683db4 · outbound

This paper cites Bop challenge 2023 on detection, segmentation and pose estimation of seen and unseen rigid objects, 2024.

GraspIT: A Dataset Bridging the Sim-to-Real gap and back for Validated Grasping SE(3) Pose Generation Bop challenge 2023 on detection, segmentation and pose estimation of seen and unseen rigid objects, 2024

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T22:15:39.843601Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 4befed48-2180-4aee-ba39-b5a6766f170c · outbound

This paper cites Effi- cient grasping from RGBD images: Learning using a new rectangle representation.

GraspIT: A Dataset Bridging the Sim-to-Real gap and back for Validated Grasping SE(3) Pose Generation Effi- cient grasping from RGBD images: Learning using a new rectangle representation

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T22:15:39.804772Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation bef16e27-5f49-4eba-b7ff-2d008f218931 · outbound

This paper cites HomebrewedDB: RGB-D Dataset for 6D Pose Estimation of 3D Objects.

GraspIT: A Dataset Bridging the Sim-to-Real gap and back for Validated Grasping SE(3) Pose Generation HomebrewedDB: RGB-D Dataset for 6D Pose Estimation of 3D Objects

Reference 17

Resolution: verified exact; local_arxiv, observed 2026-07-08T22:15:39.347704Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation e1be8311-64ea-416a-8dce-f6366de88dcd · outbound

This paper cites DROID: A large-scale in-the-wild robot manipulation dataset, 2024.

GraspIT: A Dataset Bridging the Sim-to-Real gap and back for Validated Grasping SE(3) Pose Generation DROID: A large-scale in-the-wild robot manipulation dataset, 2024

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T22:15:39.839834Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 0af6a8ff-f1e0-424a-b4e5-a9a09db20a88 · outbound

This paper cites Openvla: An open- source vision-language-action model, 2024.

GraspIT: A Dataset Bridging the Sim-to-Real gap and back for Validated Grasping SE(3) Pose Generation Openvla: An open- source vision-language-action model, 2024

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T22:15:39.798123Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 671d1757-af5b-4112-93eb-ae4b35c867e1 · outbound

This paper cites Towards robot-assisted data generation with minimal user in- teraction for autonomously training 6d pose estimation in op- erational environments.Procedia CIRP, 120:249–254, 2023.

GraspIT: A Dataset Bridging the Sim-to-Real gap and back for Validated Grasping SE(3) Pose Generation Towards robot-assisted data generation with minimal user in- teraction for autonomously training 6d pose estimation in op- erational environments.Procedia CIRP, 120:249–254, 2023

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T22:15:39.803660Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 525fe8d3-e480-43ac-9236-943b059ea00a · outbound

This paper cites Mvip – a dataset and methods for application oriented multi-view and multi-modal industrial part recognition, 2025.

GraspIT: A Dataset Bridging the Sim-to-Real gap and back for Validated Grasping SE(3) Pose Generation Mvip – a dataset and methods for application oriented multi-view and multi-modal industrial part recognition, 2025

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T22:15:39.811523Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation cdc34d6f-353d-4ed8-9baf-7b9cad7c6a89 · outbound

This paper cites Abc: A big cad model dataset for geometric deep learning, 2019.

GraspIT: A Dataset Bridging the Sim-to-Real gap and back for Validated Grasping SE(3) Pose Generation Abc: A big cad model dataset for geometric deep learning, 2019

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T22:15:39.802783Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation ef775271-b816-4d8b-b3b8-df9dfbd42f07 · outbound

This paper cites Rasim: A range-aware high- fidelity rgb-d data simulation pipeline for real-world appli- cations, 2024.

GraspIT: A Dataset Bridging the Sim-to-Real gap and back for Validated Grasping SE(3) Pose Generation Rasim: A range-aware high- fidelity rgb-d data simulation pipeline for real-world appli- cations, 2024

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T22:15:39.824601Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 6a697dd5-1f0e-4b85-a05e-ce6ddc4165dc · outbound

This paper cites Dex-net 2.0: Deep learning to plan robust grasps with synthetic point clouds and analytic grasp metrics, 2017.

GraspIT: A Dataset Bridging the Sim-to-Real gap and back for Validated Grasping SE(3) Pose Generation Dex-net 2.0: Deep learning to plan robust grasps with synthetic point clouds and analytic grasp metrics, 2017

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T22:15:39.793336Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 200c2897-4b7b-48ea-8cb7-575279239e44 · outbound

This paper cites Learning ambidextrous robot grasping policies.Sci- ence Robotics, 4(26):eaau4984, 2019.

GraspIT: A Dataset Bridging the Sim-to-Real gap and back for Validated Grasping SE(3) Pose Generation Learning ambidextrous robot grasping policies.Sci- ence Robotics, 4(26):eaau4984, 2019

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T22:15:39.820024Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 5a4f4bf6-529f-46b3-a05a-75c8a6cb3f41 · outbound

This paper cites Isaac gym: High performance GPU-based physics simulation for robot learning.

GraspIT: A Dataset Bridging the Sim-to-Real gap and back for Validated Grasping SE(3) Pose Generation Isaac gym: High performance GPU-based physics simulation for robot learning

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T22:15:39.786961Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation ed565714-e7e0-4590-8b23-78e23bcaafb6 · outbound

This paper cites GR00T N1: An open foundation model for gener- alist humanoid robots, 2025.

GraspIT: A Dataset Bridging the Sim-to-Real gap and back for Validated Grasping SE(3) Pose Generation GR00T N1: An open foundation model for gener- alist humanoid robots, 2025

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T22:15:39.823285Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 017a606a-d8e4-497f-908a-3144cc158995 · outbound

This paper cites Lula robot description and xrdf editor - isaac sim documentation, 2025.

GraspIT: A Dataset Bridging the Sim-to-Real gap and back for Validated Grasping SE(3) Pose Generation Lula robot description and xrdf editor - isaac sim documentation, 2025

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T22:15:39.829088Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 81764fe2-70c8-4ba0-b092-2d79bc66abcd · outbound

This paper cites Octo: An open-source generalist robot pol- icy, 2024.

GraspIT: A Dataset Bridging the Sim-to-Real gap and back for Validated Grasping SE(3) Pose Generation Octo: An open-source generalist robot pol- icy, 2024

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T22:15:39.822400Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation b7a9e505-5e9a-4c44-a356-7691b77cbc96 · outbound

This paper cites Colla., Abhishek Padalkar, et al.

GraspIT: A Dataset Bridging the Sim-to-Real gap and back for Validated Grasping SE(3) Pose Generation Colla., Abhishek Padalkar, et al

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T22:15:39.787139Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 610eb3a7-204b-4e2e-a264-7bd632f38c3d · outbound

This paper cites Learning Transferable Visual Models From Natural Language Supervision.

GraspIT: A Dataset Bridging the Sim-to-Real gap and back for Validated Grasping SE(3) Pose Generation Learning Transferable Visual Models From Natural Language Supervision

Reference 31

Resolution: verified exact; local_arxiv, observed 2026-07-08T22:15:39.329692Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 5cf797d3-869b-496d-94e9-933e43d55f1d · outbound

This paper cites Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks.

GraspIT: A Dataset Bridging the Sim-to-Real gap and back for Validated Grasping SE(3) Pose Generation Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

Reference 32

Resolution: metadata mismatch; local_arxiv, observed 2026-07-08T22:15:39.353184Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation e5ac222d-3a38-4d4f-99fc-cbf535c7b390 · outbound

This paper cites Indoor segmentation and support inference from rgbd images.

GraspIT: A Dataset Bridging the Sim-to-Real gap and back for Validated Grasping SE(3) Pose Generation Indoor segmentation and support inference from rgbd images

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T22:15:39.778492Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation bf5c4280-1225-4d7e-9fd9-1a28f3a326ff · outbound

This paper cites cuRobo: Parallelized Collision-Free Minimum-Jerk Robot Motion Generation.

GraspIT: A Dataset Bridging the Sim-to-Real gap and back for Validated Grasping SE(3) Pose Generation cuRobo: Parallelized Collision-Free Minimum-Jerk Robot Motion Generation

Reference 34

Resolution: verified exact; local_arxiv, observed 2026-07-08T22:15:39.352274Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 17e6ba1a-0109-401c-8c7b-28af5fb47c29 · outbound

This paper cites Contact-GraspNet: Efficient 6-DoF Grasp Generation in Cluttered Scenes.

GraspIT: A Dataset Bridging the Sim-to-Real gap and back for Validated Grasping SE(3) Pose Generation Contact-GraspNet: Efficient 6-DoF Grasp Generation in Cluttered Scenes

Reference 35

Resolution: verified exact; local_arxiv, observed 2026-07-08T22:15:39.346890Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation ac5176fe-7b36-4d66-974f-8a342823bcf1 · outbound

This paper cites Domain randomization for transferring deep neural networks from simulation to the real world.

GraspIT: A Dataset Bridging the Sim-to-Real gap and back for Validated Grasping SE(3) Pose Generation Domain randomization for transferring deep neural networks from simulation to the real world

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T22:15:39.817944Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 0a62a92d-fc62-4693-8358-b6dafa8c41bf · outbound

This paper cites Siglip 2: Multilingual vision- language encoders with improved semantic understanding, localization, and dense features.

GraspIT: A Dataset Bridging the Sim-to-Real gap and back for Validated Grasping SE(3) Pose Generation Siglip 2: Multilingual vision- language encoders with improved semantic understanding, localization, and dense features

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T22:15:39.789373Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 8d098da9-d73a-413f-b22e-17f9ae21cf3c · outbound

This paper cites BridgeData V2: A dataset for robot learning at scale, 2023.

GraspIT: A Dataset Bridging the Sim-to-Real gap and back for Validated Grasping SE(3) Pose Generation BridgeData V2: A dataset for robot learning at scale, 2023

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T22:15:39.808923Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 541192e0-fa1e-4379-99aa-f7ce118f1f5b · outbound

This paper cites Native and compact structured latents for 3d generation, 2025.

GraspIT: A Dataset Bridging the Sim-to-Real gap and back for Validated Grasping SE(3) Pose Generation Native and compact structured latents for 3d generation, 2025

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T22:15:39.782582Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation fdf5d922-05e6-4516-841f-12458b271b75 · outbound

This paper cites PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenes.

GraspIT: A Dataset Bridging the Sim-to-Real gap and back for Validated Grasping SE(3) Pose Generation PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenes

Reference 40

Resolution: verified exact; local_arxiv, observed 2026-07-08T22:15:39.345037Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation ad6f5edf-61fd-43f4-ab47-e2cb73464ffb · outbound

This paper cites Understanding the impact of geometric foundation models on vision-language-action models, 2026.

GraspIT: A Dataset Bridging the Sim-to-Real gap and back for Validated Grasping SE(3) Pose Generation Understanding the impact of geometric foundation models on vision-language-action models, 2026

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T22:15:39.839649Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 9190c19e-580d-436f-9f93-a19567dd512c · outbound

This paper cites 3D diffusion policy: Generalizable visuomotor policy learning via simple 3D representations,.

GraspIT: A Dataset Bridging the Sim-to-Real gap and back for Validated Grasping SE(3) Pose Generation 3D diffusion policy: Generalizable visuomotor policy learning via simple 3D representations,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-07-08T22:15:39.825392Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Pith citing papers

No inbound Pith citation observations are available.