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Paper Citation Record · LEDGER

DeGuV: Depth-Guided Visual Reinforcement Learning for Generalization and Interpretability in Manipulation

As of 14 August 2026, this Paper Citation Record lists 41 of 41 outbound references and 0 inbound Pith citation observations for arXiv:2509.04970.

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

pith.paper-citation-record.v1
2509.04970 v1

Coverage vector

measured 41 of 41 reference resolution

Typed states for the displayed outbound observations.

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measured 41 of 41 standing notices

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measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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

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Reference resolution

41 of 41 outbound references displayed

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Outbound references

Observation 834c816d-f5a9-471c-8810-56c9dc618bb0 · outbound

This paper cites Human-level Atari 200x faster.

DeGuV: Depth-Guided Visual Reinforcement Learning for Generalization and Interpretability in Manipulation Human-level Atari 200x faster

Reference 1

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Observation 7368e07e-2675-422f-a529-ade1d5c456df · outbound

This paper cites Fast and Data-Efficient Training of Rainbow: an Experimental Study on Atari.

DeGuV: Depth-Guided Visual Reinforcement Learning for Generalization and Interpretability in Manipulation Fast and Data-Efficient Training of Rainbow: an Experimental Study on Atari

Reference 2

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Observation ee62392f-bf61-4c5b-b157-a1cfa40f56dc · outbound

This paper cites Agent57: Outperforming the atari human benchmark,.

DeGuV: Depth-Guided Visual Reinforcement Learning for Generalization and Interpretability in Manipulation Agent57: Outperforming the atari human benchmark,

Reference 3

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Observation 8fdabd39-6bb4-4bed-a15e-c75386d51767 · outbound

This paper cites Learning to Navigate in Complex Environments.

DeGuV: Depth-Guided Visual Reinforcement Learning for Generalization and Interpretability in Manipulation Learning to Navigate in Complex Environments

Reference 4

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Observation bed4ca72-5234-4675-95cc-7672d09b1426 · outbound

This paper cites Target-driven visual navigation in indoor scenes using deep reinforcement learning,.

DeGuV: Depth-Guided Visual Reinforcement Learning for Generalization and Interpretability in Manipulation Target-driven visual navigation in indoor scenes using deep reinforcement learning,

Reference 5

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Observation 69bc0d8c-5277-4505-8ce8-f429f75ab070 · outbound

This paper cites End-to- end urban driving by imitating a reinforcement learning coach,.

DeGuV: Depth-Guided Visual Reinforcement Learning for Generalization and Interpretability in Manipulation End-to- end urban driving by imitating a reinforcement learning coach,

Reference 6

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Observation 35a3553a-6580-435f-b0ca-67efc6aa4dc9 · outbound

This paper cites Solving Rubik's Cube with a Robot Hand.

DeGuV: Depth-Guided Visual Reinforcement Learning for Generalization and Interpretability in Manipulation Solving Rubik's Cube with a Robot Hand

Reference 7

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Observation 733168d0-a378-43b9-a9dd-b02322f02123 · outbound

This paper cites Learning agile soccer skills for a bipedal robot with deep reinforcement learning,.

DeGuV: Depth-Guided Visual Reinforcement Learning for Generalization and Interpretability in Manipulation Learning agile soccer skills for a bipedal robot with deep reinforcement learning,

Reference 8

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Observation 7c82470f-dadf-43b1-89ca-00922b62142d · outbound

This paper cites Look closer: Bridging egocentric and third-person views with transformers for robotic manipulation,.

DeGuV: Depth-Guided Visual Reinforcement Learning for Generalization and Interpretability in Manipulation Look closer: Bridging egocentric and third-person views with transformers for robotic manipulation,

Reference 9

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Observation 5006c095-0957-4749-9d1c-c64905e5c4ec · outbound

This paper cites A Study on Overfitting in Deep Reinforcement Learning.

DeGuV: Depth-Guided Visual Reinforcement Learning for Generalization and Interpretability in Manipulation A Study on Overfitting in Deep Reinforcement Learning

Reference 10

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Observation 159e13bc-7bad-4239-b2bf-3053bc869985 · outbound

This paper cites MaDi: Learning to Mask Distractions for Generalization in Visual Deep Reinforcement Learning.

DeGuV: Depth-Guided Visual Reinforcement Learning for Generalization and Interpretability in Manipulation MaDi: Learning to Mask Distractions for Generalization in Visual Deep Reinforcement Learning

Reference 11

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Observation 32dbce5f-0d00-4541-8f8f-7a4856b76966 · outbound

This paper cites Stabilizing deep q-learning with convnets and vision transformers under data augmentation,.

DeGuV: Depth-Guided Visual Reinforcement Learning for Generalization and Interpretability in Manipulation Stabilizing deep q-learning with convnets and vision transformers under data augmentation,

Reference 12

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Observation 22036992-f6a9-4ac0-af83-0041a7c71c71 · outbound

This paper cites Dream to generalize: Zero-shot model- based reinforcement learning for unseen visual distractions,.

DeGuV: Depth-Guided Visual Reinforcement Learning for Generalization and Interpretability in Manipulation Dream to generalize: Zero-shot model- based reinforcement learning for unseen visual distractions,

Reference 13

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Observation eb543ea5-a9d7-4d28-87e4-56fd4e3f9cbf · outbound

This paper cites Look where you look! saliency-guided q-networks for generalization in visual reinforcement learning,.

DeGuV: Depth-Guided Visual Reinforcement Learning for Generalization and Interpretability in Manipulation Look where you look! saliency-guided q-networks for generalization in visual reinforcement learning,

Reference 14

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Observation 7e08bd72-de2f-48df-b64a-94b96a15a174 · outbound

This paper cites Learning to Manipulate Anywhere: A Visual Generalizable Framework For Reinforcement Learning.

DeGuV: Depth-Guided Visual Reinforcement Learning for Generalization and Interpretability in Manipulation Learning to Manipulate Anywhere: A Visual Generalizable Framework For Reinforcement Learning

Reference 15

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Observation e70d5f24-20e4-4e5c-a406-a47679f7c735 · outbound

This paper cites Au- tomatic data augmentation for generalization in reinforcement learn- ing,.

DeGuV: Depth-Guided Visual Reinforcement Learning for Generalization and Interpretability in Manipulation Au- tomatic data augmentation for generalization in reinforcement learn- ing,

Reference 16

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Observation c113c5e9-ed27-4888-9395-3f0b095d027c · outbound

This paper cites Generalization in reinforcement learning by soft data augmentation,.

DeGuV: Depth-Guided Visual Reinforcement Learning for Generalization and Interpretability in Manipulation Generalization in reinforcement learning by soft data augmentation,

Reference 17

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Observation 0ae0bea1-349a-4052-a928-b5257a8cf386 · outbound

This paper cites ViSaRL: Visual Reinforcement Learning Guided by Human Saliency.

DeGuV: Depth-Guided Visual Reinforcement Learning for Generalization and Interpretability in Manipulation ViSaRL: Visual Reinforcement Learning Guided by Human Saliency

Reference 18

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Observation b9b5e0f6-6815-46c1-8a6b-4989402829b9 · outbound

This paper cites Reinforcement learning with augmented data,.

DeGuV: Depth-Guided Visual Reinforcement Learning for Generalization and Interpretability in Manipulation Reinforcement learning with augmented data,

Reference 19

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Observation 85a4cff6-1ff4-4f37-870b-ba146cb08f52 · outbound

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

DeGuV: Depth-Guided Visual Reinforcement Learning for Generalization and Interpretability in Manipulation Image Augmentation Is All You Need: Regularizing Deep Reinforcement Learning from Pixels

Reference 20

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Observation 1305e1e2-53c3-4b7c-beb6-c527e8e86c62 · outbound

This paper cites Rl-vigen: A reinforcement learning benchmark for visual generalization,.

DeGuV: Depth-Guided Visual Reinforcement Learning for Generalization and Interpretability in Manipulation Rl-vigen: A reinforcement learning benchmark for visual generalization,

Reference 21

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Observation 7e140c8a-263b-402a-acc8-05b49d24a6a6 · outbound

This paper cites Contrastive Behavioral Similarity Embeddings for Generalization in Reinforcement Learning.

DeGuV: Depth-Guided Visual Reinforcement Learning for Generalization and Interpretability in Manipulation Contrastive Behavioral Similarity Embeddings for Generalization in Reinforcement Learning

Reference 22

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Observation e0cf318a-50fd-4762-a849-4971ae10f71d · outbound

This paper cites Curl: Contrastive unsupervised representations for reinforcement learning,.

DeGuV: Depth-Guided Visual Reinforcement Learning for Generalization and Interpretability in Manipulation Curl: Contrastive unsupervised representations for reinforcement learning,

Reference 23

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Observation 1cb5a067-df0f-45fb-a046-f77d4618893b · outbound

This paper cites Decomposing the generalization gap in imitation learning for visual robotic manipulation,.

DeGuV: Depth-Guided Visual Reinforcement Learning for Generalization and Interpretability in Manipulation Decomposing the generalization gap in imitation learning for visual robotic manipulation,

Reference 24

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Observation b2d6fe48-4615-4207-9e37-fb78ce839f52 · outbound

This paper cites Domain adaptation in reinforcement learning via latent unified state representation,.

DeGuV: Depth-Guided Visual Reinforcement Learning for Generalization and Interpretability in Manipulation Domain adaptation in reinforcement learning via latent unified state representation,

Reference 25

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Observation a05a8228-43cd-4284-9606-49e73a335eff · outbound

This paper cites Pre- trained image encoder for generalizable visual reinforcement learning,.

DeGuV: Depth-Guided Visual Reinforcement Learning for Generalization and Interpretability in Manipulation Pre- trained image encoder for generalizable visual reinforcement learning,

Reference 26

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Observation e64f51a8-6123-4e6a-b2f3-dbf0ca8969fe · outbound

This paper cites Focus-then-decide: segmentation-assisted reinforcement learning,.

DeGuV: Depth-Guided Visual Reinforcement Learning for Generalization and Interpretability in Manipulation Focus-then-decide: segmentation-assisted reinforcement learning,

Reference 27

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Observation b7c9e8f5-077b-4240-84a8-8692ff2b1362 · outbound

This paper cites Segment Anything.

DeGuV: Depth-Guided Visual Reinforcement Learning for Generalization and Interpretability in Manipulation Segment Anything

Reference 28

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This paper cites Mask-based latent reconstruction for reinforcement learning,.

DeGuV: Depth-Guided Visual Reinforcement Learning for Generalization and Interpretability in Manipulation Mask-based latent reconstruction for reinforcement learning,

Reference 29

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Observation feb4673c-53cc-4065-83d9-dc365bcaaf50 · outbound

This paper cites Ignorance is bliss: Robust control via information gating,.

DeGuV: Depth-Guided Visual Reinforcement Learning for Generalization and Interpretability in Manipulation Ignorance is bliss: Robust control via information gating,

Reference 30

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Observation 6d459143-26d3-4dc4-b514-966a52f8cd0a · outbound

This paper cites An A* Curriculum Approach to Reinforcement Learning for RGBD Indoor Robot Navigation.

DeGuV: Depth-Guided Visual Reinforcement Learning for Generalization and Interpretability in Manipulation An A* Curriculum Approach to Reinforcement Learning for RGBD Indoor Robot Navigation

Reference 31

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Observation 0c199ebe-226b-43fa-a33b-cdebb56a567b · outbound

This paper cites Robotic grasping using deep reinforcement learning,.

DeGuV: Depth-Guided Visual Reinforcement Learning for Generalization and Interpretability in Manipulation Robotic grasping using deep reinforcement learning,

Reference 32

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Observation 6a4ea837-457d-4ef2-8a79-6288b50980be · outbound

This paper cites A dqn-based autonomous car-following framework using rgb-d frames,.

DeGuV: Depth-Guided Visual Reinforcement Learning for Generalization and Interpretability in Manipulation A dqn-based autonomous car-following framework using rgb-d frames,

Reference 33

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Observation b6a0c445-0e1e-46ff-b1bb-074bed20be73 · outbound

This paper cites Q-attention: Enabling efficient learning for vision-based robotic manipulation,.

DeGuV: Depth-Guided Visual Reinforcement Learning for Generalization and Interpretability in Manipulation Q-attention: Enabling efficient learning for vision-based robotic manipulation,

Reference 34

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No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation 6561f26d-a9ab-43d4-b92f-007ea27983ee · outbound

This paper cites Soft actor-critic: Off- policy maximum entropy deep reinforcement learning with a stochastic actor,.

DeGuV: Depth-Guided Visual Reinforcement Learning for Generalization and Interpretability in Manipulation Soft actor-critic: Off- policy maximum entropy deep reinforcement learning with a stochastic actor,

Reference 35

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unresolved
no resolver link, observed 2026-08-05T05:47:32.718017Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T05:47:32.718017Z digest=sha256:53d27f947cfc0904e6db0bc21866840e25245a02d090c051386745af08282754

Observation eae1de48-03cf-46ad-bee4-58ab12000104 · outbound

This paper cites Learning to predict by the methods of temporal differences,.

DeGuV: Depth-Guided Visual Reinforcement Learning for Generalization and Interpretability in Manipulation Learning to predict by the methods of temporal differences,

Reference 36

Resolution
unresolved
no resolver link, observed 2026-08-05T05:47:32.739288Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T05:47:32.739288Z digest=sha256:9b6e820cf7044fac986dd3b939530103d9b691889032057ff08e8ef5e484e8e0

Observation 82205a5f-1f1a-43fb-b3ba-caf3c4da1998 · outbound

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

DeGuV: Depth-Guided Visual Reinforcement Learning for Generalization and Interpretability in Manipulation Mastering Visual Continuous Control: Improved Data-Augmented Reinforcement Learning

Reference 37

Resolution
unresolved
no resolver link, observed 2026-08-05T05:47:32.798343Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T05:47:32.798343Z digest=sha256:20cb17dfb95ef6946a4adf6b6ba2ddffbadaebd4cf9dc415239dbc121a65d4bc

Observation 245c043d-bd37-45ef-b3da-a449e026eabf · outbound

This paper cites Representation Learning with Contrastive Predictive Coding.

DeGuV: Depth-Guided Visual Reinforcement Learning for Generalization and Interpretability in Manipulation Representation Learning with Contrastive Predictive Coding

Reference 38

Resolution
unresolved
no resolver link, observed 2026-08-05T05:47:32.855513Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T05:47:32.855513Z digest=sha256:6b0df7378a86bd0a08af850222e0facb7988075468de3ed67972318b446faa47

Observation bcbed3e4-bbe0-4647-ac57-fb4a818848a0 · outbound

This paper cites robosuite: A Modular Simulation Framework and Benchmark for Robot Learning.

DeGuV: Depth-Guided Visual Reinforcement Learning for Generalization and Interpretability in Manipulation robosuite: A Modular Simulation Framework and Benchmark for Robot Learning

Reference 39

Resolution
unresolved
no resolver link, observed 2026-08-05T05:47:32.944548Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T05:47:32.944548Z digest=sha256:97b18c7808140601d8c16a9526afc1845dd571a8e49d0dead196902ce0ca400d

Observation 32a1991f-5079-4290-b13f-e0c60d144f17 · outbound

This paper cites Simple copy-paste is a strong data augmentation method for instance segmentation,.

DeGuV: Depth-Guided Visual Reinforcement Learning for Generalization and Interpretability in Manipulation Simple copy-paste is a strong data augmentation method for instance segmentation,

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T05:47:33.977816Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-05T05:47:33.003581Z digest=sha256:eb38f6574b9d0401553d3c4f9e5d778b7cd9181b411bdd2e827743c7b3485b42

Observation d453469d-6df9-4a1c-bfcb-6e3760be47de · outbound

This paper cites Taming the panda with python: A powerful duo for seamless robotics programming and integration,.

DeGuV: Depth-Guided Visual Reinforcement Learning for Generalization and Interpretability in Manipulation Taming the panda with python: A powerful duo for seamless robotics programming and integration,

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T05:47:33.861485Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-05T05:47:33.119172Z digest=sha256:ac9f07bc5e36985451809d4bcde27bef6709a4d5120f1617458f1af9778d71fd

Pith citing papers

No inbound Pith citation observations are available.