Paper Citation Record · LEDGER

A Visual Reinforcement Learning-Based Separate Primitive Policy for Peg-in-Hole Tasks

As of 5 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 0 inbound Pith citation observations for arXiv:2504.14820.

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

pith.paper-citation-record.v1
2504.14820 v2

Coverage vector

measured 36 of 36 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-22T18:43:18.064214Z

measured 36 of 36 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-05T06:32:48.257954+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

36 of 36 outbound references displayed

verified exact4
verified fuzzy32
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation c86dbc2b-b60a-43a0-9677-4192d94af7a7 · outbound

This paper cites Pre-Trained Image Encoder for Generalizable Visual Reinforcement Learning.

A Visual Reinforcement Learning-Based Separate Primitive Policy for Peg-in-Hole Tasks Pre-Trained Image Encoder for Generalizable Visual Reinforcement Learning

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T18:45:09.374896Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation c55b741e-466a-43f0-9101-9b6b46e4ff08 · outbound

This paper cites PolyFit: A Peg-in-hole Assembly Framework for Unseen Polygon Shapes via Sim-to-real Adaptation.

A Visual Reinforcement Learning-Based Separate Primitive Policy for Peg-in-Hole Tasks PolyFit: A Peg-in-hole Assembly Framework for Unseen Polygon Shapes via Sim-to-real Adaptation

Reference 2

Resolution: verified exact; arxiv_id, observed 2026-05-22T18:45:03.512255Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation aefe96d0-ec87-4c92-8d58-a858a7c9bc14 · outbound

This paper cites The Power of the Senses: Generalizable Manipulation from Vision and Touch through Masked Multimodal Learning.

A Visual Reinforcement Learning-Based Separate Primitive Policy for Peg-in-Hole Tasks The Power of the Senses: Generalizable Manipulation from Vision and Touch through Masked Multimodal Learning

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-05-22T18:45:03.522105Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 2a72dabb-f79b-41b8-943a-f23739a9c365 · outbound

This paper cites On Pre-Training for Visuo-Motor Control: Revisiting a Learning-from-Scratch Baseline.

A Visual Reinforcement Learning-Based Separate Primitive Policy for Peg-in-Hole Tasks On Pre-Training for Visuo-Motor Control: Revisiting a Learning-from-Scratch Baseline

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T18:45:09.358909Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 3557d355-77f7-402e-9f4d-b6b672805cb8 · outbound

This paper cites Learning to Manipulate Anywhere: A Visual Generalizable Framework For Re- inforcement Learning.

A Visual Reinforcement Learning-Based Separate Primitive Policy for Peg-in-Hole Tasks Learning to Manipulate Anywhere: A Visual Generalizable Framework For Re- inforcement Learning

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T18:45:09.356441Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 41a8f9d1-a339-4445-acdf-19211e04c21d · outbound

This paper cites Stabilizing Deep Q-Learning with ConvNets and Vision Transformers under Data Augmentation.

A Visual Reinforcement Learning-Based Separate Primitive Policy for Peg-in-Hole Tasks Stabilizing Deep Q-Learning with ConvNets and Vision Transformers under Data Augmentation

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T18:45:09.309933Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation c37d03a1-f63c-45eb-915e-5c67830c680c · outbound

This paper cites Augmenting Reinforcement Learn- ing with Behavior Primitives for Diverse Manipulation Tasks.

A Visual Reinforcement Learning-Based Separate Primitive Policy for Peg-in-Hole Tasks Augmenting Reinforcement Learn- ing with Behavior Primitives for Diverse Manipulation Tasks

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T18:45:09.340663Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 31518bff-f928-4390-9d2a-063d5c0eeb2b · outbound

This paper cites Learning Sequences of Manip- ulation Primitives for Robotic Assembly.

A Visual Reinforcement Learning-Based Separate Primitive Policy for Peg-in-Hole Tasks Learning Sequences of Manip- ulation Primitives for Robotic Assembly

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T18:45:09.336114Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 21139e98-54c0-4b6a-a5a8-1052cc1d4ad7 · outbound

This paper cites Randomized Ensembled Double Q-Learning: Learning Fast Without a Model.

A Visual Reinforcement Learning-Based Separate Primitive Policy for Peg-in-Hole Tasks Randomized Ensembled Double Q-Learning: Learning Fast Without a Model

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T18:45:09.333607Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation b89482f2-9529-4ee0-be32-d885cad874e7 · outbound

This paper cites Revisiting Plasticity in Visual Reinforcement Learning: Data, Modules and Training Stages.

A Visual Reinforcement Learning-Based Separate Primitive Policy for Peg-in-Hole Tasks Revisiting Plasticity in Visual Reinforcement Learning: Data, Modules and Training Stages

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T18:45:09.323490Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation a60da414-d03d-45fe-9b35-bb39f2e28eb0 · outbound

This paper cites Reinforcement Learning of Impedance Policies for Peg-in-Hole Tasks: Role of Asymmetric Matrices.

A Visual Reinforcement Learning-Based Separate Primitive Policy for Peg-in-Hole Tasks Reinforcement Learning of Impedance Policies for Peg-in-Hole Tasks: Role of Asymmetric Matrices

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T18:45:09.325780Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 6358d6a7-9b2f-47f0-b1ef-547b6ec05138 · outbound

This paper cites Benchmarking Protocols for Evaluating Small Parts Robotic Assem- bly Systems.

A Visual Reinforcement Learning-Based Separate Primitive Policy for Peg-in-Hole Tasks Benchmarking Protocols for Evaluating Small Parts Robotic Assem- bly Systems

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T18:45:09.395356Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation fe1b6b53-1ae5-4711-aacf-68b6ea485228 · outbound

This paper cites Multimodality Driven Impedance-Based Sim2Real Transfer Learning for Robotic Multiple Peg-in-Hole Assembly.

A Visual Reinforcement Learning-Based Separate Primitive Policy for Peg-in-Hole Tasks Multimodality Driven Impedance-Based Sim2Real Transfer Learning for Robotic Multiple Peg-in-Hole Assembly

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T18:45:09.391034Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 080a38c2-c2d9-49da-a4f1-2ac2c052e4bc · outbound

This paper cites Visual-Force- Tactile Fusion for Gentle Intricate Insertion Tasks.

A Visual Reinforcement Learning-Based Separate Primitive Policy for Peg-in-Hole Tasks Visual-Force- Tactile Fusion for Gentle Intricate Insertion Tasks

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T18:45:09.386811Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 1ffe06d4-1487-4e2b-a55d-d839c9d745c0 · outbound

This paper cites Tactile-RL for Insertion: Generalization to Objects of Un- known Geometry.

A Visual Reinforcement Learning-Based Separate Primitive Policy for Peg-in-Hole Tasks Tactile-RL for Insertion: Generalization to Objects of Un- known Geometry

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T18:45:09.304733Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 57076245-b6e6-4900-adf4-3c3ee449c147 · outbound

This paper cites Reinforcement Learning on Variable Impedance Con- troller for High-Precision Robotic Assembly.

A Visual Reinforcement Learning-Based Separate Primitive Policy for Peg-in-Hole Tasks Reinforcement Learning on Variable Impedance Con- troller for High-Precision Robotic Assembly

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T18:45:09.382344Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation b841c3eb-9dbf-4085-b9d0-25eafd0b1420 · outbound

This paper cites TacSL: A Library for Visuotactile Sensor Simulation and Learning.

A Visual Reinforcement Learning-Based Separate Primitive Policy for Peg-in-Hole Tasks TacSL: A Library for Visuotactile Sensor Simulation and Learning

Reference 17

Resolution: verified exact; arxiv_id, observed 2026-05-22T18:45:03.539935Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 8fe4c6c6-a991-44dd-8e2e-b3bf6dbbc3fd · outbound

This paper cites Vi- sual Spatial Attention and Proprioceptive Data-Driven Reinforcement Learning for Robust Peg-in-Hole Task Under Variable Conditions.

A Visual Reinforcement Learning-Based Separate Primitive Policy for Peg-in-Hole Tasks Vi- sual Spatial Attention and Proprioceptive Data-Driven Reinforcement Learning for Robust Peg-in-Hole Task Under Variable Conditions

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T18:45:09.379901Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 3f5d61f8-a75a-4f6d-a812-7ac3b0828a14 · outbound

This paper cites Proactive Action Visual Residual Reinforcement Learning for Contact-Rich Tasks Using a Torque-Controlled Robot.

A Visual Reinforcement Learning-Based Separate Primitive Policy for Peg-in-Hole Tasks Proactive Action Visual Residual Reinforcement Learning for Contact-Rich Tasks Using a Torque-Controlled Robot

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T18:45:09.354077Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 7eb2fac8-0939-49b8-8c77-33de57c9f054 · outbound

This paper cites Automate: Specialist and Generalist Assembly Policies over Diverse Geometries.

A Visual Reinforcement Learning-Based Separate Primitive Policy for Peg-in-Hole Tasks Automate: Specialist and Generalist Assembly Policies over Diverse Geometries

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T18:45:09.288030Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 44c96b37-9ab5-43c3-8683-33cd7f06e66e · outbound

This paper cites Learning Insertion Primitives with Discrete-Continuous Hybrid Action Space for Robotic Assembly Tasks.

A Visual Reinforcement Learning-Based Separate Primitive Policy for Peg-in-Hole Tasks Learning Insertion Primitives with Discrete-Continuous Hybrid Action Space for Robotic Assembly Tasks

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T18:45:09.306549Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation bbe0a612-051b-47c0-a0f4-6816c7e06097 · outbound

This paper cites H-InDex: Visual Reinforcement Learning with Hand-Informed Representations for Dexterous Manipulation.

A Visual Reinforcement Learning-Based Separate Primitive Policy for Peg-in-Hole Tasks H-InDex: Visual Reinforcement Learning with Hand-Informed Representations for Dexterous Manipulation

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T18:45:09.399261Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation eb60115f-07ea-4424-8764-c2a117f7dcd7 · outbound

This paper cites Look where you look! Saliency-guided Q-networks for generalization in visual Reinforcement Learning.

A Visual Reinforcement Learning-Based Separate Primitive Policy for Peg-in-Hole Tasks Look where you look! Saliency-guided Q-networks for generalization in visual Reinforcement Learning

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T18:45:09.298146Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation ef5ae5fd-85d5-476f-b372-7fc6ecf63690 · outbound

This paper cites Reinforcement Learning with Augmented Data.

A Visual Reinforcement Learning-Based Separate Primitive Policy for Peg-in-Hole Tasks Reinforcement Learning with Augmented Data

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T18:45:09.367825Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 97ad8a5b-235e-4a80-9bf8-7e9bbe7dfe54 · outbound

This paper cites Image Augmentation Is All You Need: Regularizing Deep Reinforcement Learning from Pixels.

A Visual Reinforcement Learning-Based Separate Primitive Policy for Peg-in-Hole Tasks Image Augmentation Is All You Need: Regularizing Deep Reinforcement Learning from Pixels

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T18:45:09.302208Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 917a26bc-e2f4-4413-a69b-0ce054e9409c · outbound

This paper cites Mastering Visual Con- tinuous Control: Improved Data-Augmented Reinforcement Learning.

A Visual Reinforcement Learning-Based Separate Primitive Policy for Peg-in-Hole Tasks Mastering Visual Con- tinuous Control: Improved Data-Augmented Reinforcement Learning

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T18:45:09.280698Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation f57e358c-f20b-4d9f-9311-0fe2991b8550 · outbound

This paper cites A Recipe for Unbounded Data Augmentation in Visual Reinforcement Learning.

A Visual Reinforcement Learning-Based Separate Primitive Policy for Peg-in-Hole Tasks A Recipe for Unbounded Data Augmentation in Visual Reinforcement Learning

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T18:45:09.394148Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 328a250c-f855-4047-9700-dbf96512cade · outbound

This paper cites Taco: Temporal Latent Action-Driven Contrastive Loss for Visual Reinforcement Learning.

A Visual Reinforcement Learning-Based Separate Primitive Policy for Peg-in-Hole Tasks Taco: Temporal Latent Action-Driven Contrastive Loss for Visual Reinforcement Learning

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T18:45:09.321712Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation c95e873c-b001-46af-b05b-4e8674f707fd · outbound

This paper cites R3m: A Universal Visual Representation for Robot Manipulation.

A Visual Reinforcement Learning-Based Separate Primitive Policy for Peg-in-Hole Tasks R3m: A Universal Visual Representation for Robot Manipulation

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T18:45:09.364286Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 385316ac-2ae3-436d-b328-23ad05e48505 · outbound

This paper cites Robots Pre-train Robots: Manipulation-Centric Robotic Representation from Large-Scale Robot Datasets.

A Visual Reinforcement Learning-Based Separate Primitive Policy for Peg-in-Hole Tasks Robots Pre-train Robots: Manipulation-Centric Robotic Representation from Large-Scale Robot Datasets

Reference 30

Resolution: verified exact; arxiv_id, observed 2026-05-22T18:45:03.504211Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 305c701b-d675-40ca-828f-a10422310604 · outbound

This paper cites A markovian decision process.

A Visual Reinforcement Learning-Based Separate Primitive Policy for Peg-in-Hole Tasks A markovian decision process

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T18:45:09.361132Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 92d16b88-04e8-4207-9573-cf7538d18e71 · outbound

This paper cites Continuous control with deep reinforce- ment learning.

A Visual Reinforcement Learning-Based Separate Primitive Policy for Peg-in-Hole Tasks Continuous control with deep reinforce- ment learning

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T18:45:09.270844Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation a57c4710-0259-44fa-bfe2-77b607148218 · outbound

This paper cites Addressing function approxi- mation error in actor-critic methods.

A Visual Reinforcement Learning-Based Separate Primitive Policy for Peg-in-Hole Tasks Addressing function approxi- mation error in actor-critic methods

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T18:45:09.351411Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation d36e7d1c-0872-45ac-aa43-97b6a1ddd876 · outbound

This paper cites One policy to control them all: Shared modular policies for agent-agnostic control.

A Visual Reinforcement Learning-Based Separate Primitive Policy for Peg-in-Hole Tasks One policy to control them all: Shared modular policies for agent-agnostic control

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T18:45:09.272500Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 8c29eac7-9a06-475e-a473-54109a334f61 · outbound

This paper cites Active Vision Reinforcement Learning under Limited Visual Observability.

A Visual Reinforcement Learning-Based Separate Primitive Policy for Peg-in-Hole Tasks Active Vision Reinforcement Learning under Limited Visual Observability

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T18:45:09.345298Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 2d5ed323-2f27-43e9-9d14-d7e4561a2352 · outbound

This paper cites A unified approach for motion and force control of robot manipulators: The operational space formulation.

A Visual Reinforcement Learning-Based Separate Primitive Policy for Peg-in-Hole Tasks A unified approach for motion and force control of robot manipulators: The operational space formulation

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-05-22T18:45:09.375866Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Pith citing papers

No inbound Pith citation observations are available.