Paper Citation Record · LEDGER

Learning to Grasp from 2.5D images: a Deep Reinforcement Learning Approach

As of 20 August 2026, this Paper Citation Record lists 17 of 17 outbound references and 0 inbound Pith citation observations for arXiv:1908.03440.

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

pith.paper-citation-record.v1
1908.03440 v1

Coverage vector

measured 17 of 17 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-14T14:31:47.180785Z

measured 17 of 17 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+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

17 of 17 outbound references displayed

verified exact0
verified fuzzy11
unresolved6
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 341add0f-a829-43c4-b39b-f81418c31ee8 · outbound

This paper cites Unity: A General Platform for Intelligent Agents.

Learning to Grasp from 2.5D images: a Deep Reinforcement Learning Approach Unity: A General Platform for Intelligent Agents

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-14T14:31:47.109548Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2053a8ae-8a94-4702-bd57-f7ba045c3d9d · outbound

This paper cites Playing atari with deep reinforcement learning,.

Learning to Grasp from 2.5D images: a Deep Reinforcement Learning Approach Playing atari with deep reinforcement learning,

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-14T14:31:47.115113Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1f76de33-3f4f-4cac-856f-21eee9ad2dff · outbound

This paper cites Deeploco: Dynamic locomotion skills using hierarchical deep reinforcement learning,.

Learning to Grasp from 2.5D images: a Deep Reinforcement Learning Approach Deeploco: Dynamic locomotion skills using hierarchical deep reinforcement learning,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:31:47.396426Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 2637c453-9ee3-4a62-86cf-eda05eb9ff50 · outbound

This paper cites Gibson env: Real-world perception for embod- ied agents,.

Learning to Grasp from 2.5D images: a Deep Reinforcement Learning Approach Gibson env: Real-world perception for embod- ied agents,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:31:47.384125Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation c434c427-e30e-4059-b992-f01949300bc3 · outbound

This paper cites Deep reinforcement learning for vision-based robotic grasping: A simulated comparative evaluation of off- policy methods,.

Learning to Grasp from 2.5D images: a Deep Reinforcement Learning Approach Deep reinforcement learning for vision-based robotic grasping: A simulated comparative evaluation of off- policy methods,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:31:47.372472Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation d6775ca3-0a4b-4d67-996d-df484254000f · outbound

This paper cites Learning Complex Dexterous Manipulation with Deep Reinforcement Learning and Demonstrations.

Learning to Grasp from 2.5D images: a Deep Reinforcement Learning Approach Learning Complex Dexterous Manipulation with Deep Reinforcement Learning and Demonstrations

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-14T14:31:47.134504Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7a444c70-6051-468d-8892-2a96ba5d73cd · outbound

This paper cites A natural policy gradient,.

Learning to Grasp from 2.5D images: a Deep Reinforcement Learning Approach A natural policy gradient,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:31:47.360361Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 7896459e-4ac6-404d-8b62-a4cb9fb1bfe9 · outbound

This paper cites Learning to ﬂy by combining reinforcement learning with behavioural cloning,.

Learning to Grasp from 2.5D images: a Deep Reinforcement Learning Approach Learning to ﬂy by combining reinforcement learning with behavioural cloning,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:31:47.350338Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 694ac118-ad95-486e-ad01-3d3b011546cd · outbound

This paper cites Asymmetric actor critic for image-based robot learning,.

Learning to Grasp from 2.5D images: a Deep Reinforcement Learning Approach Asymmetric actor critic for image-based robot learning,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:31:47.338336Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 927d6c05-fbf7-4ae6-bd35-f2c7f3effaf9 · outbound

This paper cites Sim-to-Real Robot Learning from Pixels with Progressive Nets.

Learning to Grasp from 2.5D images: a Deep Reinforcement Learning Approach Sim-to-Real Robot Learning from Pixels with Progressive Nets

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-14T14:31:47.152906Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a5e2ad09-776a-4ce1-9a37-812e95c8979d · outbound

This paper cites Actor-critic algorithms,.

Learning to Grasp from 2.5D images: a Deep Reinforcement Learning Approach Actor-critic algorithms,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:31:47.323646Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation ec257d00-d44a-49f1-a413-3c0aee5991b2 · outbound

This paper cites Continuous control with deep reinforcement learning,.

Learning to Grasp from 2.5D images: a Deep Reinforcement Learning Approach Continuous control with deep reinforcement learning,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:31:47.311426Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation ea006e64-435b-4c1d-bb1f-883c2cea0ce1 · outbound

This paper cites Deep reinforcement learning that matters,.

Learning to Grasp from 2.5D images: a Deep Reinforcement Learning Approach Deep reinforcement learning that matters,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:31:47.298973Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 8a9a1edc-c4b4-47b0-b2ad-1242d97d1a32 · outbound

This paper cites Deep reinforcement learning: A brief survey,.

Learning to Grasp from 2.5D images: a Deep Reinforcement Learning Approach Deep reinforcement learning: A brief survey,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:31:47.286949Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation f58b0d69-145b-4ece-b883-6a5e7d2af56d · outbound

This paper cites Trust Region Policy Optimization.

Learning to Grasp from 2.5D images: a Deep Reinforcement Learning Approach Trust Region Policy Optimization

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-14T14:31:47.171742Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 35d23269-6635-48ec-8512-90e56f6c7350 · outbound

This paper cites Proximal Policy Optimization Algorithms.

Learning to Grasp from 2.5D images: a Deep Reinforcement Learning Approach Proximal Policy Optimization Algorithms

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-14T14:31:47.176051Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e5ee0c14-7c20-4fd8-902d-19c2f8b6fd6c · outbound

This paper cites Cur- riculum learning,.

Learning to Grasp from 2.5D images: a Deep Reinforcement Learning Approach Cur- riculum learning,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:31:47.273412Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Pith citing papers

No inbound Pith citation observations are available.