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Paper Citation Record · LEDGER

Sim-to-Real Transfer in Deep Reinforcement Learning for Robotics: a Survey

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:2009.13303.

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

pith.paper-citation-record.v1
2009.13303 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 8 of 8 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-04T05:51:29.482999Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T00:39:16.665490Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 1c011582-2d4a-4b50-b07a-b4809f124fc8 · inbound

Mind the Sim2Real Gap in User Simulation for Agentic Tasks cites this paper.

Mind the Sim2Real Gap in User Simulation for Agentic Tasks Sim-to-Real Transfer in Deep Reinforcement Learning for Robotics: a Survey

Reference 43

Resolution
unresolved
no resolver link, observed 2026-08-04T05:51:29.482999Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-04T05:51:29.482999Z digest=sha256:9ac31e04fd10bf34ec4c24fe24813ada91376f68ebd740a80762affa9af65fd3

Observation a97dae7f-3ab2-4044-8735-02c16bbf5f00 · inbound

EmbodiedGovBench: A Benchmark for Governance, Recovery, and Upgrade Safety in Embodied Agent Systems cites this paper.

EmbodiedGovBench: A Benchmark for Governance, Recovery, and Upgrade Safety in Embodied Agent Systems Sim-to-Real Transfer in Deep Reinforcement Learning for Robotics: a Survey

Reference 23

Resolution
verified exact
arxiv_id, observed 2026-05-11T10:41:06.756347Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-05-10T15:21:20.231759Z digest=sha256:3e67e16ed4785c6d5972333a8326fba0ebf043e486e1fca12afb2daa1a89f74c

Observation 5024bfc2-6323-438d-be1f-c9f132b88d28 · inbound

A Unified Causal-Origin Taxonomy of Distributional Shifts in Reinforcement Learning cites this paper.

A Unified Causal-Origin Taxonomy of Distributional Shifts in Reinforcement Learning Sim-to-Real Transfer in Deep Reinforcement Learning for Robotics: a Survey

Reference 67

Resolution
unresolved
no resolver link, observed 2026-07-12T13:42:27.758405Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-12T13:42:27.758405Z digest=sha256:4f0c09d1962112d70fc0fa9d2aa262a13f729bfb83b678a370acd85675db5fb8

Observation 48ad46e5-3a88-49cf-8e4f-303e2db50cc7 · inbound

Object-Centric Residual RL for Zero-Shot Sim-to-Real VLA Enhancement cites this paper.

Object-Centric Residual RL for Zero-Shot Sim-to-Real VLA Enhancement Sim-to-Real Transfer in Deep Reinforcement Learning for Robotics: a Survey

Reference 24

Resolution
verified exact
arxiv_id, observed 2026-07-04T00:39:16.667380Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-06-26T21:06:48.539679Z digest=sha256:03dc260d980fe28272055a7262ace220011b3b3b9626b501469ad3439f10aa05

Observation c77a1dba-47d5-4595-acb3-0b2c299726d4 · inbound

Vision-Language-Action Models: Experimental Insights from a Real-World UR5 Platform cites this paper.

Vision-Language-Action Models: Experimental Insights from a Real-World UR5 Platform Sim-to-Real Transfer in Deep Reinforcement Learning for Robotics: a Survey

Reference 28

Resolution
verified exact
arxiv_id, observed 2026-06-30T14:34:45.891812Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-06-30T05:25:16.143939Z digest=sha256:1c6579a747e66058c498eb83ef023506e4dc5a750e5e70184139eeee2740eb07

Observation 5d47acb8-000c-4b07-a7c3-680fbe6df790 · inbound

Deep Reinforcement Learning for Individual Atomic Control and Cooling cites this paper.

Deep Reinforcement Learning for Individual Atomic Control and Cooling Sim-to-Real Transfer in Deep Reinforcement Learning for Robotics: a Survey

Reference 38

Resolution
verified exact
arxiv_id, observed 2026-07-01T12:45:44.040556Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-07-01T01:52:16.084081Z digest=sha256:0f356cc19e849f5041bc51d9d683dfb43de7020381ebf3e233cb2c120f87b43b

Observation 8b16ae45-74be-4cf5-b641-1681d17c8011 · inbound

Efficient Sim-to-Real Transfer of World-Action Models from Synthetic Priors cites this paper.

Efficient Sim-to-Real Transfer of World-Action Models from Synthetic Priors Sim-to-Real Transfer in Deep Reinforcement Learning for Robotics: a Survey

Reference 12

Resolution
metadata mismatch
arxiv_id, observed 2026-07-01T10:05:41.166264Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-07-01T05:51:20.153785Z digest=sha256:e39eb4423fb0c563b09a662ae55b51d037bda13523885b7bd5dfa8af5d2affe2

Observation ea0d028a-c360-4ee2-a97d-d250e2d02dee · inbound

Situation Aware Frontier Prioritization for Quadruped Search and Rescue cites this paper.

Situation Aware Frontier Prioritization for Quadruped Search and Rescue Sim-to-Real Transfer in Deep Reinforcement Learning for Robotics: a Survey

Reference 5

Resolution
unresolved
no resolver link, observed 2026-08-04T04:31:57.980867Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-04T04:31:57.980867Z digest=sha256:98a15781902cd606df35e5eec70fc0b34e99175b639c44685c90888133b5d3b7