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Paper Citation Record · LEDGER

A Survey of Sim-to-Real Methods in RL: Progress, Prospects and Challenges with Foundation Models

As of 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 26 inbound Pith citation observations for arXiv:2502.13187.

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

pith.paper-citation-record.v1
2502.13187 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 26 of 26 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 26 of 26 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T04:17:35.711854Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
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  • malformed identifier0
  • metadata mismatch0

External citation measurements

3
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 3f20851a-ba71-43a7-af1d-095e670e23a8 · inbound

Linear Mixture Distributionally Robust Markov Decision Processes cites this paper.

Linear Mixture Distributionally Robust Markov Decision Processes A Survey of Sim-to-Real Methods in RL: Progress, Prospects and Challenges with Foundation Models

Reference 6

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no resolver link, observed 2026-08-07T14:44:10.442723Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:44:10.442723Z digest=sha256:42a10e41cc1a69b2a79785e8557ff918dbefd8cd5a70ead3382a6b605805281d

Observation 08742330-b3db-42ca-9b0f-ccdfdacc7295 · inbound

EgoWalk: A Multimodal Dataset for Robot Navigation in the Wild cites this paper.

EgoWalk: A Multimodal Dataset for Robot Navigation in the Wild A Survey of Sim-to-Real Methods in RL: Progress, Prospects and Challenges with Foundation Models

Reference 10

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arxiv_id, observed 2026-05-19T12:57:17.706290Z

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.

source=pdf_text observed=2026-05-19T12:56:30.634284Z digest=sha256:215b8229e9c8453adf5a678f111e9ccf83fc98561157984e5d6f84b6c86d58c3

Observation 0b4d867d-06c9-46e9-9839-6fa1b1746dd0 · inbound

Learning human-to-robot handovers through 3D scene reconstruction cites this paper.

Learning human-to-robot handovers through 3D scene reconstruction A Survey of Sim-to-Real Methods in RL: Progress, Prospects and Challenges with Foundation Models

Reference 2

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no resolver link, observed 2026-08-06T18:18:24.090778Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:18:24.090778Z digest=sha256:cb74af1ff62d72765e71698784e3552bfc303d6d3e2dc39a686a4dbb9b88bef9

Observation 04f81a39-7905-450e-8685-76876da0f0fb · inbound

Foundation Model Driven Robotics: A Comprehensive Review cites this paper.

Foundation Model Driven Robotics: A Comprehensive Review A Survey of Sim-to-Real Methods in RL: Progress, Prospects and Challenges with Foundation Models

Reference 148

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no resolver link, observed 2026-08-06T17:43:53.388999Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T17:43:53.388999Z digest=sha256:c10702764b50b19ba724f820a8cae44bbc5803f2c31c82656b2cb7d1d0790d3e

Observation a7565bb3-2bb0-4432-b7ee-cd291d012079 · inbound

DeepShade: Enable Shade Simulation by Text-conditioned Image Generation cites this paper.

DeepShade: Enable Shade Simulation by Text-conditioned Image Generation A Survey of Sim-to-Real Methods in RL: Progress, Prospects and Challenges with Foundation Models

Reference 2025

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no resolver link, observed 2026-08-06T16:58:56.932404Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T16:58:56.932404Z digest=sha256:fbe1bb4f298440a1494a2626a1cda9ac958f2904923f5b6063ecbcf71b68b9ed

Observation e61c1c92-5146-46da-be41-d937434ff500 · inbound

Joint-Local Grounded Action Transformation for Sim-to-Real Transfer in Multi-Agent Traffic Control cites this paper.

Joint-Local Grounded Action Transformation for Sim-to-Real Transfer in Multi-Agent Traffic Control A Survey of Sim-to-Real Methods in RL: Progress, Prospects and Challenges with Foundation Models

Reference 2020

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unresolved
no resolver link, observed 2026-08-06T15:43:44.184999Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T15:43:44.184999Z digest=sha256:baceb1fd9c90a18291caea1c5bb497f93f5f46f57632848a224ec0f0f009d81c

Observation 82eed66f-a399-4593-9a7b-cbc81c49c252 · inbound

SSRL: Self-Search Reinforcement Learning cites this paper.

SSRL: Self-Search Reinforcement Learning A Survey of Sim-to-Real Methods in RL: Progress, Prospects and Challenges with Foundation Models

Reference 6

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no resolver link, observed 2026-08-05T20:17:07.610161Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T20:17:07.610161Z digest=sha256:40bded4c9c769c2ff7b290f4bd94d5e15c702b18f23d55f41f8d402c9c7444c8

Observation e9399922-4783-477c-9770-6ba8b7aaab1d · inbound

UniCon: A Unified System for Efficient Robot Learning Transfers cites this paper.

UniCon: A Unified System for Efficient Robot Learning Transfers A Survey of Sim-to-Real Methods in RL: Progress, Prospects and Challenges with Foundation Models

Reference 4

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verified exact
arxiv_id, observed 2026-05-16T13:00:54.985687Z

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.

source=pdf_text observed=2026-05-16T12:58:56.857747Z digest=sha256:782c0771fba806cbe894446cacd8e8f221569ad442e9d67d7eef011919cd17cc

Observation 53f8afac-37cf-47ba-87d5-e8e7dd5cf81d · inbound

Rationality Measurement and Theory for Reinforcement Learning Agents cites this paper.

Rationality Measurement and Theory for Reinforcement Learning Agents A Survey of Sim-to-Real Methods in RL: Progress, Prospects and Challenges with Foundation Models

Reference 4

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metadata mismatch
arxiv_id, observed 2026-05-16T07:20:43.658831Z

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.

source=pdf_text observed=2026-05-16T07:20:22.600233Z digest=sha256:391ee08dde0f5b557a4c3ca6660b80380a84168121c1553800aba49655bd90e2

Observation 5e27ab1a-ae7a-42ef-b01a-7660a12b580b · inbound

Rationality Measurement and Theory for Reinforcement Learning Agents cites this paper.

Rationality Measurement and Theory for Reinforcement Learning Agents A Survey of Sim-to-Real Methods in RL: Progress, Prospects and Challenges with Foundation Models

Reference 1996

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unresolved
no resolver link, observed 2026-08-03T04:34:32.557538Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T04:34:32.557538Z digest=sha256:11868a5333162b7e45b85241ec143f5767b91075726439c0ff398aa4baffedfc

Observation 3d26a7df-e95c-4a28-b545-9538e9e627e7 · inbound

Mitigating Data Scarcity in Spaceflight Applications for Offline Reinforcement Learning Using Physics-Informed Deep Generative Models cites this paper.

Mitigating Data Scarcity in Spaceflight Applications for Offline Reinforcement Learning Using Physics-Informed Deep Generative Models A Survey of Sim-to-Real Methods in RL: Progress, Prospects and Challenges with Foundation Models

Reference 16

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verified exact
arxiv_id, observed 2026-05-13T21:38:18.474239Z

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.

source=pdf_text observed=2026-05-13T21:35:52.012244Z digest=sha256:0f4d589397be020bc39c89aab6ff616176a181a0f33af118e006def89057a93e

Observation 1fd222e6-8763-4277-b193-d324dfdee8ea · inbound

Application of Deep Reinforcement Learning to Event-Triggered Control for Networked Artificial Pancreas Systems cites this paper.

Application of Deep Reinforcement Learning to Event-Triggered Control for Networked Artificial Pancreas Systems A Survey of Sim-to-Real Methods in RL: Progress, Prospects and Challenges with Foundation Models

Reference 20

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verified exact
arxiv_id, observed 2026-05-12T00:41:16.716357Z

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.

source=pdf_text observed=2026-05-07T14:51:30.263897Z digest=sha256:60fda201d78a69aea9dd38fb921b6f4bc14397ffe129743cb69ec8b8a3476366

Observation a5300bb2-76ac-44e2-9aad-3ef3ffcd2d0e · inbound

Application of Deep Reinforcement Learning to Event-Triggered Control for Networked Artificial Pancreas Systems cites this paper.

Application of Deep Reinforcement Learning to Event-Triggered Control for Networked Artificial Pancreas Systems A Survey of Sim-to-Real Methods in RL: Progress, Prospects and Challenges with Foundation Models

Reference 20

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verified exact
arxiv_id, observed 2026-05-19T17:47:41.642187Z

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.

source=pdf_text observed=2026-05-19T17:46:20.907461Z digest=sha256:958c21255d31deab200fef9a4558091d1639f2e703f77eab7a497f3ec34a73bc

Observation ccd626ff-6364-4570-aba8-78ed87f46692 · inbound

Sim-to-Real Transfer and Robustness Evaluation of Reinforcement Learning Control with Integrated Perception on an ASV for Floating Waste Capture cites this paper.

Sim-to-Real Transfer and Robustness Evaluation of Reinforcement Learning Control with Integrated Perception on an ASV for Floating Waste Capture A Survey of Sim-to-Real Methods in RL: Progress, Prospects and Challenges with Foundation Models

Reference 45

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verified exact
arxiv_id, observed 2026-05-09T06:20:41.607118Z

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.

source=pdf_text observed=2026-05-08T18:35:31.104891Z digest=sha256:e1440ca40b67fe6ca47d6010acf587a817e74f0978ff829c66c20c7d3cee6870

Observation 30868728-8ad1-4648-a2f3-8f8d22d03f7a · inbound

Decoupled Delay Compensation: Enhancing Pre-trained MARL Policies via Learned Dynamics Filtering cites this paper.

Decoupled Delay Compensation: Enhancing Pre-trained MARL Policies via Learned Dynamics Filtering A Survey of Sim-to-Real Methods in RL: Progress, Prospects and Challenges with Foundation Models

Reference 2

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verified exact
arxiv_id, observed 2026-06-29T19:03:51.156987Z

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.

source=pdf_text observed=2026-06-29T19:02:39.358444Z digest=sha256:2ad03c37a26ee20ae90c04fb3dbe9fbedf5703177203ac9646e065a5f23e3234

Observation 5fe4d8d1-09b7-43d8-a0db-dd719b6eaf0b · inbound

The Sim-to-Real Gap of Foundation Model Agents: A Unified MDP Perspective cites this paper.

The Sim-to-Real Gap of Foundation Model Agents: A Unified MDP Perspective A Survey of Sim-to-Real Methods in RL: Progress, Prospects and Challenges with Foundation Models

Reference 16

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verified exact
arxiv_id, observed 2026-07-02T16:47:09.256195Z

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.

source=pdf_text observed=2026-06-27T22:26:44.624473Z digest=sha256:810d6f85bb52bbc77b617ce11be0ef8cd2ea74836a405f9d3543009b994a798f

Observation 71bd261a-b9d5-4cf5-a3a3-c30c25b30fee · inbound

Targeting World Models to Compromise Robot Learning Pipelines cites this paper.

Targeting World Models to Compromise Robot Learning Pipelines A Survey of Sim-to-Real Methods in RL: Progress, Prospects and Challenges with Foundation Models

Reference 35

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verified exact
arxiv_id, observed 2026-06-27T16:41:03.733360Z

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.

source=pdf_text observed=2026-06-27T16:05:01.264700Z digest=sha256:cb80006c7a3610c2ebc222b3dd17cb0db26c0c9b1e4ea6bfb55c15e2b3660185

Observation 58ed23ee-71b6-40d5-aaf5-5b7d9f07fc3d · inbound

Tac-DINO: Learning Vision-Tactile Features with Patch Alignment cites this paper.

Tac-DINO: Learning Vision-Tactile Features with Patch Alignment A Survey of Sim-to-Real Methods in RL: Progress, Prospects and Challenges with Foundation Models

Reference 130

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verified exact
arxiv_id, observed 2026-07-03T09:47:59.725974Z

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.

source=arxiv_source observed=2026-06-27T10:19:44.434418Z digest=sha256:851e79ed9a8a3b9e83af6de9d0151c35083ddc2f103f18e4d26637b4c424d970

Observation c89b6354-5b88-4c35-bf5b-c331c9513df1 · inbound

In LLM Reasoning, there is Irrationality on top of Value Misalignment cites this paper.

In LLM Reasoning, there is Irrationality on top of Value Misalignment A Survey of Sim-to-Real Methods in RL: Progress, Prospects and Challenges with Foundation Models

Reference 32

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metadata mismatch
arxiv_id, observed 2026-06-29T18:03:48.430484Z

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.

source=arxiv_source observed=2026-06-29T17:37:05.306678Z digest=sha256:5996ff5d15103fb00b93393ee727fbb8e846ace8b7dcb1c2d852909bf08ae922

Observation 6c414fb5-a19b-408d-b04d-8700756c17eb · inbound

IDEA: Insensitive to Dynamics Mismatch via Effect Alignment for Sim-to-Real Transfer in Multi-Agent Control cites this paper.

IDEA: Insensitive to Dynamics Mismatch via Effect Alignment for Sim-to-Real Transfer in Multi-Agent Control A Survey of Sim-to-Real Methods in RL: Progress, Prospects and Challenges with Foundation Models

Reference 3

Resolution
verified exact
arxiv_id, observed 2026-07-04T12:59:52.860420Z

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.

source=pdf_text observed=2026-06-26T05:35:46.096869Z digest=sha256:8c64bdcab9f7c9cc0083a342b5e0be697471e6ace5b53abe0074a40f697a237d

Observation b03ba206-de1f-4beb-9380-fe9be5d9bc90 · inbound

FADA: Few-Shot Domain Adaptation via Dynamics Alignment for Humanoid Control cites this paper.

FADA: Few-Shot Domain Adaptation via Dynamics Alignment for Humanoid Control A Survey of Sim-to-Real Methods in RL: Progress, Prospects and Challenges with Foundation Models

Reference 32

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arxiv_id, observed 2026-06-30T01:34:09.984468Z

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.

source=arxiv_source observed=2026-06-30T01:28:29.441778Z digest=sha256:68964a0a2b143a12f24dddbf69ccc0e6cd9392509831634b8ed855bcacca2c8d

Observation fab31d8e-7175-45bd-af90-2706d3f1784d · inbound

DADiff: Diffusion-Driven Cross-Domain Policy Adaptation for Reinforcement Learning cites this paper.

DADiff: Diffusion-Driven Cross-Domain Policy Adaptation for Reinforcement Learning A Survey of Sim-to-Real Methods in RL: Progress, Prospects and Challenges with Foundation Models

Reference 2

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no resolver link, observed 2026-08-01T21:28:11.692533Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T21:28:11.692533Z digest=sha256:62ceb88b694f665594112b47550f4b07922d765140ad9d1689c72688fa6394dc

Observation 42ed115b-4e9f-40f5-bde1-3bae0ca57054 · inbound

Continual-RL for Generalization in Autonomous Racing on the RoboRacer Platform cites this paper.

Continual-RL for Generalization in Autonomous Racing on the RoboRacer Platform A Survey of Sim-to-Real Methods in RL: Progress, Prospects and Challenges with Foundation Models

Reference 21

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no resolver link, observed 2026-07-31T18:18:00.234775Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-31T18:18:00.234775Z digest=sha256:7aff122940739d592fd3d9bf2922d61c5ef5ba352adf99ca02eb62cdb2452f6c

Observation 91903daa-a3f8-4e2d-92e7-a3e91dcd4762 · inbound

Data Pyramid for Embodied Manipulation: A Survey cites this paper.

Data Pyramid for Embodied Manipulation: A Survey A Survey of Sim-to-Real Methods in RL: Progress, Prospects and Challenges with Foundation Models

Reference 67

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no resolver link, observed 2026-07-31T06:18:55.387779Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-31T06:18:55.387779Z digest=sha256:535859c34075f9c34e62d696089b85b73a00d999011964681eac42b3ee418853

Observation 47a5397d-5e5c-48ed-8c46-03a9920a867a · inbound

Foundations of Reinforcement Learning and Control:Connections and New Perspectives cites this paper.

Foundations of Reinforcement Learning and Control:Connections and New Perspectives A Survey of Sim-to-Real Methods in RL: Progress, Prospects and Challenges with Foundation Models

Reference 19

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no resolver link, observed 2026-08-04T07:32:30.080353Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T07:32:30.080353Z digest=sha256:ac3f5882ef86e81a677a7b9c28729033e6e0a99a35a66a37bc47a0bbb7d9bcce

Observation 2c372c4a-143e-4907-926d-9af0e223c5b7 · inbound

AndroidReality: How Far Are Mobile Agents from the Real World? cites this paper.

AndroidReality: How Far Are Mobile Agents from the Real World? A Survey of Sim-to-Real Methods in RL: Progress, Prospects and Challenges with Foundation Models

Reference 15

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no resolver link, observed 2026-08-11T04:17:35.711854Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T04:17:35.711854Z digest=sha256:81a00ce4b20c88fcc61ddfa369deb31a717a4057d3ed09df9bd4cba52eb8fee4