Paper Citation Record · LEDGER

RoboGround: Robotic Manipulation with Grounded Vision-Language Priors

As of 19 August 2026, this Paper Citation Record lists 55 of 55 outbound references and 1 inbound Pith citation observation for arXiv:2504.21530.

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

pith.paper-citation-record.v1
2504.21530 v1

Coverage vector

measured 55 of 55 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T05:07:05.277615Z

measured 56 of 56 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T00:57:26.612635Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-07T00:57:31.251745Z

Reference resolution

55 of 55 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation f074e8e6-36cd-4847-9400-191526bc441e · outbound

This paper cites GPT-4 Technical Report.

RoboGround: Robotic Manipulation with Grounded Vision-Language Priors GPT-4 Technical Report

Reference 1

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Source-reported events for the cited work

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Observation 0d461be6-308f-4049-b4c4-d4bf31ce7dd9 · outbound

This paper cites RT-H: Action Hierarchies Using Language.

RoboGround: Robotic Manipulation with Grounded Vision-Language Priors RT-H: Action Hierarchies Using Language

Reference 2

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Observation 0ef83400-73fb-493f-aebc-6736262fa895 · outbound

This paper cites Track2act: Predicting point tracks from internet videos enables diverse zero-shot robot manip- ulation, 2024.

RoboGround: Robotic Manipulation with Grounded Vision-Language Priors Track2act: Predicting point tracks from internet videos enables diverse zero-shot robot manip- ulation, 2024

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:07:06.109718Z

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Observation 4836559d-3557-4069-804f-920f4084e771 · outbound

This paper cites Zero-Shot Robotic Manipulation with Pretrained Image-Editing Diffusion Models.

RoboGround: Robotic Manipulation with Grounded Vision-Language Priors Zero-Shot Robotic Manipulation with Pretrained Image-Editing Diffusion Models

Reference 4

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Observation 8ce6648c-e311-409c-9a1d-44ef3561d056 · outbound

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

RoboGround: Robotic Manipulation with Grounded Vision-Language Priors RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control

Reference 5

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Observation 2824a6d8-0541-4ffe-89e4-b040ff4cfe62 · outbound

This paper cites Closed-Loop Visuomotor Control with Generative Expectation for Robotic Manipulation.

RoboGround: Robotic Manipulation with Grounded Vision-Language Priors Closed-Loop Visuomotor Control with Generative Expectation for Robotic Manipulation

Reference 6

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Source-reported events for the cited work

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Observation 46e141c7-077d-4b04-9206-4332712dbd01 · outbound

This paper cites Shikra: Unleashing multimodal llm’s referential dialogue magic, 2023.

RoboGround: Robotic Manipulation with Grounded Vision-Language Priors Shikra: Unleashing multimodal llm’s referential dialogue magic, 2023

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:07:06.093942Z

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Observation 69db985e-c967-4495-bffa-78c239e509c5 · outbound

This paper cites Diffusion policy: Visuomotor policy learning via action dif- fusion.

RoboGround: Robotic Manipulation with Grounded Vision-Language Priors Diffusion policy: Visuomotor policy learning via action dif- fusion

Reference 8

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Observation 61ba1ce3-9d6c-4b66-b236-ad5704926dfb · outbound

This paper cites Instructblip: Towards general- purpose vision-language models with instruction tuning,.

RoboGround: Robotic Manipulation with Grounded Vision-Language Priors Instructblip: Towards general- purpose vision-language models with instruction tuning,

Reference 9

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Observation cb84a066-c793-414a-9616-46a80439ccf0 · outbound

This paper cites Imitating Task and Motion Planning with Visuomotor Transformers.

RoboGround: Robotic Manipulation with Grounded Vision-Language Priors Imitating Task and Motion Planning with Visuomotor Transformers

Reference 10

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Observation 5fb40c13-a3c4-425a-9999-85db878aed17 · outbound

This paper cites Objaverse: A universe of annotated 3d objects.

RoboGround: Robotic Manipulation with Grounded Vision-Language Priors Objaverse: A universe of annotated 3d objects

Reference 11

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Observation ee4c90f3-db52-4217-9d5d-df1c49c4340b · outbound

This paper cites FlowBot3D: Learning 3D Articulation Flow to Manipulate Articulated Objects.

RoboGround: Robotic Manipulation with Grounded Vision-Language Priors FlowBot3D: Learning 3D Articulation Flow to Manipulate Articulated Objects

Reference 12

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Observation db5e5724-441e-4e18-a801-d1e2dfe57d50 · outbound

This paper cites Anygrasp: Robust and efficient grasp perception in spa- tial and temporal domains.

RoboGround: Robotic Manipulation with Grounded Vision-Language Priors Anygrasp: Robust and efficient grasp perception in spa- tial and temporal domains

Reference 13

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Observation 67075254-6d1b-4dfb-b3a4-b71e2eac5973 · outbound

This paper cites Moka: Open-world robotic manipulation through mark- based visual prompting.

RoboGround: Robotic Manipulation with Grounded Vision-Language Priors Moka: Open-world robotic manipulation through mark- based visual prompting

Reference 14

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Observation dce6ae1b-891a-4f92-acf5-519d726b0f15 · outbound

This paper cites RT-Trajectory: Robotic Task Generalization via Hindsight Trajectory Sketches.

RoboGround: Robotic Manipulation with Grounded Vision-Language Priors RT-Trajectory: Robotic Task Generalization via Hindsight Trajectory Sketches

Reference 15

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Observation 1c440137-15a8-49d7-8e4e-808e75f09171 · outbound

This paper cites Masked autoencoders are scalable vision learners.

RoboGround: Robotic Manipulation with Grounded Vision-Language Priors Masked autoencoders are scalable vision learners

Reference 16

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Observation 94e26d11-60ca-461b-9501-8ae73a4335c9 · outbound

This paper cites Perceiver: General perception with iterative attention.

RoboGround: Robotic Manipulation with Grounded Vision-Language Priors Perceiver: General perception with iterative attention

Reference 17

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Observation 2064a1ad-e8e5-4fd1-90f3-81836ef34b23 · outbound

This paper cites Rlbench: The robot learning benchmark & learning environment.

RoboGround: Robotic Manipulation with Grounded Vision-Language Priors Rlbench: The robot learning benchmark & learning environment

Reference 18

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Observation 6dcf33ae-c757-4c84-9011-f240efd95410 · outbound

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

RoboGround: Robotic Manipulation with Grounded Vision-Language Priors OpenVLA: An Open-Source Vision-Language-Action Model

Reference 19

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Observation 2a032ea2-8a3c-4f81-9829-da414ccef191 · outbound

This paper cites Segment Anything.

RoboGround: Robotic Manipulation with Grounded Vision-Language Priors Segment Anything

Reference 20

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Observation cb65e6f4-5702-4e09-908b-9d2c06834f18 · outbound

This paper cites Lisa: Reasoning segmentation via large language model.

RoboGround: Robotic Manipulation with Grounded Vision-Language Priors Lisa: Reasoning segmentation via large language model

Reference 21

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Observation 8b745074-d37b-41b5-bb85-14f9fa64895e · outbound

This paper cites BLIP-2: bootstrapping language-image pre-training with frozen image encoders and large language models.

RoboGround: Robotic Manipulation with Grounded Vision-Language Priors BLIP-2: bootstrapping language-image pre-training with frozen image encoders and large language models

Reference 22

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This paper cites Visual instruction tuning, 2023.

RoboGround: Robotic Manipulation with Grounded Vision-Language Priors Visual instruction tuning, 2023

Reference 23

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Observation e4e6f5ae-d174-4aec-aec3-fd7c21ea9676 · outbound

This paper cites Groma: Localized visual tokenization for grounding multimodal large language models.

RoboGround: Robotic Manipulation with Grounded Vision-Language Priors Groma: Localized visual tokenization for grounding multimodal large language models

Reference 24

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Observation 03b09d70-72d5-49f7-8dcf-899593fbd439 · outbound

This paper cites What Matters in Learning from Offline Human Demonstrations for Robot Manipulation.

RoboGround: Robotic Manipulation with Grounded Vision-Language Priors What Matters in Learning from Offline Human Demonstrations for Robot Manipulation

Reference 25

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Observation d7ad62f4-ff13-4623-9aaf-bdb8c8051f59 · outbound

This paper cites MimicGen: A Data Generation System for Scalable Robot Learning using Human Demonstrations.

RoboGround: Robotic Manipulation with Grounded Vision-Language Priors MimicGen: A Data Generation System for Scalable Robot Learning using Human Demonstrations

Reference 26

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This paper cites Calvin: A benchmark for language- conditioned policy learning for long-horizon robot manip- ulation tasks.

RoboGround: Robotic Manipulation with Grounded Vision-Language Priors Calvin: A benchmark for language- conditioned policy learning for long-horizon robot manip- ulation tasks

Reference 27

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This paper cites RoboCasa: Large-Scale Simulation of Everyday Tasks for Generalist Robots.

RoboGround: Robotic Manipulation with Grounded Vision-Language Priors RoboCasa: Large-Scale Simulation of Everyday Tasks for Generalist Robots

Reference 28

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This paper cites Kosmos-2: Ground- ing multimodal large language models to the world.

RoboGround: Robotic Manipulation with Grounded Vision-Language Priors Kosmos-2: Ground- ing multimodal large language models to the world

Reference 29

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Observation ddd45b25-6d60-4b09-a78c-13707d575483 · outbound

This paper cites Film: Visual reasoning with a general conditioning layer.

RoboGround: Robotic Manipulation with Grounded Vision-Language Priors Film: Visual reasoning with a general conditioning layer

Reference 30

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Observation b2365aef-51a9-4f86-b86b-8a121f1917be · outbound

This paper cites Learning transferable visual models from natural language supervi- sion.

RoboGround: Robotic Manipulation with Grounded Vision-Language Priors Learning transferable visual models from natural language supervi- sion

Reference 31

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This paper cites Glamm: Pixel grounding large multimodal model.

RoboGround: Robotic Manipulation with Grounded Vision-Language Priors Glamm: Pixel grounding large multimodal model

Reference 32

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Observation 4775d94b-b1f0-4892-a54a-ec08ba8c47ff · outbound

This paper cites Toolflownet: Robotic manipulation with tools via predicting tool flow from point clouds.

RoboGround: Robotic Manipulation with Grounded Vision-Language Priors Toolflownet: Robotic manipulation with tools via predicting tool flow from point clouds

Reference 33

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This paper cites Cliport: What and where pathways for robotic manipulation.

RoboGround: Robotic Manipulation with Grounded Vision-Language Priors Cliport: What and where pathways for robotic manipulation

Reference 34

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Observation c4303925-de3e-4ff7-b20f-e2d8fda771b3 · outbound

This paper cites Open-World Object Manipulation using Pre-trained Vision-Language Models.

RoboGround: Robotic Manipulation with Grounded Vision-Language Priors Open-World Object Manipulation using Pre-trained Vision-Language Models

Reference 35

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This paper cites KITE: Keypoint-Conditioned Policies for Semantic Manipulation.

RoboGround: Robotic Manipulation with Grounded Vision-Language Priors KITE: Keypoint-Conditioned Policies for Semantic Manipulation

Reference 36

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This paper cites Rt-sketch: Goal-conditioned imitation learning from hand-drawn sketches.

RoboGround: Robotic Manipulation with Grounded Vision-Language Priors Rt-sketch: Goal-conditioned imitation learning from hand-drawn sketches

Reference 37

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This paper cites Predictive Inverse Dynamics Models are Scalable Learners for Robotic Manipulation.

RoboGround: Robotic Manipulation with Grounded Vision-Language Priors Predictive Inverse Dynamics Models are Scalable Learners for Robotic Manipulation

Reference 38

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RoboGround: Robotic Manipulation with Grounded Vision-Language Priors Robotap: Tracking arbitrary points for few-shot visual imitation

Reference 39

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This paper cites GenSim: Generating Robotic Simulation Tasks via Large Language Models.

RoboGround: Robotic Manipulation with Grounded Vision-Language Priors GenSim: Generating Robotic Simulation Tasks via Large Language Models

Reference 40

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This paper cites The All-Seeing Project V2: Towards General Relation Comprehension of the Open World.

RoboGround: Robotic Manipulation with Grounded Vision-Language Priors The All-Seeing Project V2: Towards General Relation Comprehension of the Open World

Reference 41

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This paper cites Any-point Trajectory Modeling for Policy Learning.

RoboGround: Robotic Manipulation with Grounded Vision-Language Priors Any-point Trajectory Modeling for Policy Learning

Reference 42

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RoboGround: Robotic Manipulation with Grounded Vision-Language Priors Unleashing Large-Scale Video Generative Pre-training for Visual Robot Manipulation

Reference 43

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RoboGround: Robotic Manipulation with Grounded Vision-Language Priors Florence-2: Advancing a unified representation for a variety of vision tasks

Reference 44

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This paper cites Flow as the cross-domain manipulation interface.

RoboGround: Robotic Manipulation with Grounded Vision-Language Priors Flow as the cross-domain manipulation interface

Reference 45

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This paper cites Ferret: Refer and Ground Anything Anywhere at Any Granularity.

RoboGround: Robotic Manipulation with Grounded Vision-Language Priors Ferret: Refer and Ground Anything Anywhere at Any Granularity

Reference 46

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RoboGround: Robotic Manipulation with Grounded Vision-Language Priors General Flow as Foundation Affordance for Scalable Robot Learning

Reference 47

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RoboGround: Robotic Manipulation with Grounded Vision-Language Priors Ferret-v2: An Improved Baseline for Referring and Grounding with Large Language Models

Reference 48

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This paper cites Sprint: Scalable policy pre-training via language instruc- 10 tion relabeling.

RoboGround: Robotic Manipulation with Grounded Vision-Language Priors Sprint: Scalable policy pre-training via language instruc- 10 tion relabeling

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:07:05.776430Z

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RoboGround: Robotic Manipulation with Grounded Vision-Language Priors Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware

Reference 50

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RoboGround: Robotic Manipulation with Grounded Vision-Language Priors MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models

Reference 51

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RoboGround: Robotic Manipulation with Grounded Vision-Language Priors Unresolved cited work

Reference 53

Resolution: unresolved; raw_fallback, observed 2026-08-16T05:07:05.745175Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 71b8116e-2fb6-44ad-bc13-402a9bbaa1d2 · outbound

This paper cites an unresolved cited work.

RoboGround: Robotic Manipulation with Grounded Vision-Language Priors Unresolved cited work

Reference 54

Resolution: unresolved; raw_fallback, observed 2026-08-16T05:07:05.729786Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation a133c291-10b0-4dfa-b7c8-e906210a3e78 · outbound

This paper cites yellow surface with brown spots.

RoboGround: Robotic Manipulation with Grounded Vision-Language Priors yellow surface with brown spots

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:07:05.714651Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Pith citing papers

Observation 7c29c10a-ade1-4c72-91e6-45a7d04c8a88 · inbound

AntiGrounding: Lifting Robotic Actions into VLM Representation Space for Decision Making cites this paper.

AntiGrounding: Lifting Robotic Actions into VLM Representation Space for Decision Making RoboGround: Robotic Manipulation with Grounded Vision-Language Priors

Reference 18

Resolution: verified exact; local_arxiv, observed 2026-08-07T00:57:31.256067Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.