Paper Citation Record · LEDGER

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

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 25 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 25 of 25 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 25 of 25 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:44:10.442723Z

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
verified fuzzy0
unresolved0
parse uncertain0
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

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

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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

Resolution: verified exact; 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 2026-08-08T06:32:00.761636+00:00.

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

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

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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

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

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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

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

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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

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

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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

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

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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

Resolution: 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 2026-08-08T06:32:00.761636+00:00.

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

Resolution: 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 2026-08-08T06:32:00.761636+00:00.

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

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

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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

Resolution: 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 2026-08-08T06:32:00.761636+00:00.

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

Resolution: 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 2026-08-08T06:32:00.761636+00:00.

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

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

Reference 20

Resolution: 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 2026-08-08T06:32:00.761636+00:00.

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

Resolution: 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 2026-08-08T06:32:00.761636+00:00.

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

Resolution: 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 2026-08-08T06:32:00.761636+00:00.

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

Resolution: 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 2026-08-08T06:32:00.761636+00:00.

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

Resolution: 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 2026-08-08T06:32:00.761636+00:00.

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

Resolution: 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 2026-08-08T06:32:00.761636+00:00.

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

Resolution: 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 2026-08-08T06:32:00.761636+00:00.

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 2026-08-08T06:32:00.761636+00:00.

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

Resolution: verified exact; 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 2026-08-08T06:32:00.761636+00:00.

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

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

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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

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

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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

Data Pyramid for Embodied Manipulation cites this paper.

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

Reference 67

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

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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

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

Source-reported events for the cited work

Unavailable: canonical work link unavailable.