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Paper Citation Record · LEDGER

Deep Reinforcement Learning for Robotics: A Survey of Real-World Successes

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:2408.03539.

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

pith.paper-citation-record.v1
2408.03539 v3

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 2026-08-07T13:17:07.704599Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-16T12:55:40.303269Z

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 f71efbaf-08a7-48ce-99b5-42145d35d2c2 · inbound

VLA-RL: Towards Masterful and General Robotic Manipulation with Scalable Reinforcement Learning cites this paper.

VLA-RL: Towards Masterful and General Robotic Manipulation with Scalable Reinforcement Learning Deep Reinforcement Learning for Robotics: A Survey of Real-World Successes

Reference 66

Resolution
verified exact
arxiv_id, observed 2026-05-16T12:55:40.305488Z

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=2026-05-16T12:55:40.245908Z digest=sha256:cbbbd2499b502e35389b1ea0e9e2389d207f0157ceb663a1c5c039fb3030c845

Observation d2d5d553-adf2-4344-aef8-99a91d835eb0 · inbound

From Motion to Behavior: Hierarchical Modeling of Humanoid Generative Behavior Control cites this paper.

From Motion to Behavior: Hierarchical Modeling of Humanoid Generative Behavior Control Deep Reinforcement Learning for Robotics: A Survey of Real-World Successes

Reference 69

Resolution
unresolved
no resolver link, observed 2026-08-07T13:17:07.704599Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:17:07.704599Z digest=sha256:159f76668beaa9d720e0a830717383ad7ac01f5b3eded803ce026a8848785b3e

Observation c9346b67-0e30-4054-b925-b411996995bb · inbound

Fast Estimation of Globally Optimal Independent Contact Regions for Robust Grasping and Manipulation cites this paper.

Fast Estimation of Globally Optimal Independent Contact Regions for Robust Grasping and Manipulation Deep Reinforcement Learning for Robotics: A Survey of Real-World Successes

Reference 35

Resolution
unresolved
no resolver link, observed 2026-08-07T05:10:10.450532Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:10:10.450532Z digest=sha256:5cf170bb5d68880039528b2025b35848ab258a28691560797498486498ebffb0

Observation 133df37f-1da7-4a14-8587-0b0219ea0ed7 · inbound

Perspective on Utilizing Foundation Models for Laboratory Automation in Materials Research cites this paper.

Perspective on Utilizing Foundation Models for Laboratory Automation in Materials Research Deep Reinforcement Learning for Robotics: A Survey of Real-World Successes

Reference 88

Resolution
malformed identifier
no resolver link, observed 2026-08-07T00:55:17.013563Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:55:17.013563Z digest=sha256:8fb31ab57d605c130d47202b691d140d3edba52f5e9ac49c7fdd67e269dbccea

Observation 71c9dd8f-fd74-41b4-beb6-56d24296c8b6 · inbound

RL as Regressor: A Reinforcement Learning Approach for Function Approximation cites this paper.

RL as Regressor: A Reinforcement Learning Approach for Function Approximation Deep Reinforcement Learning for Robotics: A Survey of Real-World Successes

Reference 2024

Resolution
unresolved
no resolver link, observed 2026-08-06T10:22:39.899194Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T10:22:39.899194Z digest=sha256:1b8b929d6feec511223cb9019e7101fe5a6476b4b13f6a23e3bf117421b96184

Observation 2f9521a9-f171-4ad0-bdd4-1165b78b3942 · inbound

Latent Activation Editing: Inference-Time Refinement of Learned Policies for Safer Multirobot Navigation cites this paper.

Latent Activation Editing: Inference-Time Refinement of Learned Policies for Safer Multirobot Navigation Deep Reinforcement Learning for Robotics: A Survey of Real-World Successes

Reference 4

Resolution
unresolved
no resolver link, observed 2026-08-04T15:18:02.500174Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T15:18:02.500174Z digest=sha256:315be4d9412bacfe9686437741f3aa095a3ec7b338b5f4743de440e873e27cf1

Observation 066f57d9-e8a0-41f9-9e8f-50480fb642a2 · inbound

Rainbow Deep Q-Learning with Kinematics-Aware Design for Cooperative Delta and 3-RRS Parallel Robot Insertion cites this paper.

Rainbow Deep Q-Learning with Kinematics-Aware Design for Cooperative Delta and 3-RRS Parallel Robot Insertion Deep Reinforcement Learning for Robotics: A Survey of Real-World Successes

Reference 5

Resolution
metadata mismatch
arxiv_id, observed 2026-05-13T06:17:22.981802Z

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=2026-05-13T06:15:01.077535Z digest=sha256:c2d1a6d669a6bffa5838ebcff062694686f9fdfbc9f3bef35b60ecfce4cd1638

Observation 3ae82f45-9016-431e-800c-995033ea0cae · inbound

Is Inter-Seed Cross-Play Enough? Evaluating the Robustness of Zero-Shot Coordination Algorithms to Implementation Details cites this paper.

Is Inter-Seed Cross-Play Enough? Evaluating the Robustness of Zero-Shot Coordination Algorithms to Implementation Details Deep Reinforcement Learning for Robotics: A Survey of Real-World Successes

Reference 256

Resolution
unresolved
no resolver link, observed 2026-08-05T15:25:40.543647Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T15:25:40.543647Z digest=sha256:fd40c2904c6c7db0d43b9b8b633bbca847359717a087dea1600f84a56029fcc3