Paper Citation Record · LEDGER

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

As of 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 14 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 14 of 14 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 14 of 14 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-14T04:34:41.034485Z

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 534fdd08-0d02-4d4d-bc0a-d5ea9d2d8de7 · inbound

ViSTa Dataset: Do vision-language models understand sequential tasks? cites this paper.

ViSTa Dataset: Do vision-language models understand sequential tasks? Deep Reinforcement Learning for Robotics: A Survey of Real-World Successes

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-12T16:45:47.168907Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c5de9f1a-aa08-41d5-a133-6e42bd427334 · inbound

Exploring the Adversarial Vulnerabilities of Vision-Language-Action Models in Robotics cites this paper.

Exploring the Adversarial Vulnerabilities of Vision-Language-Action Models in Robotics Deep Reinforcement Learning for Robotics: A Survey of Real-World Successes

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-12T18:51:16.514756Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8f3f9179-5345-44d3-a094-fe598652c8c0 · inbound

Performance Optimization of Ratings-Based Reinforcement Learning cites this paper.

Performance Optimization of Ratings-Based Reinforcement Learning Deep Reinforcement Learning for Robotics: A Survey of Real-World Successes

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-10T20:40:11.542400Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation aef544f0-2ed0-49fc-a8fe-7713a79a1329 · inbound

SPEQ: Offline Stabilization Phases for Efficient Q-Learning in High Update-To-Data Ratio Reinforcement Learning cites this paper.

SPEQ: Offline Stabilization Phases for Efficient Q-Learning in High Update-To-Data Ratio Reinforcement Learning Deep Reinforcement Learning for Robotics: A Survey of Real-World Successes

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-10T20:24:21.914535Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 22933b28-81d1-46b0-9dda-b09224f8b04d · inbound

Differentially Private Policy Gradient cites this paper.

Differentially Private Policy Gradient Deep Reinforcement Learning for Robotics: A Survey of Real-World Successes

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-09T21:30:15.868108Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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 2026-08-18T06:34:40.430872+00:00.

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.

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.

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.

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.

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.

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 2026-08-18T06:34:40.430872+00:00.

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.

Observation 926aea1d-e8f4-4bc0-bead-dca43d7409e0 · inbound

Knowledge-Distilled End-to-End Reinforcement Learning for Smooth 6-DOF Thrust Control and Rapid Adaptation to Ocean Currents in Remotely Operated Vehicles cites this paper.

Knowledge-Distilled End-to-End Reinforcement Learning for Smooth 6-DOF Thrust Control and Rapid Adaptation to Ocean Currents in Remotely Operated Vehicles Deep Reinforcement Learning for Robotics: A Survey of Real-World Successes

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-14T04:34:41.034485Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.