Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Using Simulation and Domain Adaptation to Improve Efficiency of Deep Robotic Grasping
As of 5 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:1709.07857.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-05T06:32:48.257954+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-03T18:30:49.411746Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-05-21T18:24:18.210251Z
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 061f8e4f-d5a8-46bc-ac4f-21dd876679e8 · inbound
State-Conditional Adversarial Learning: An Off-Policy Visual Domain Transfer Method for End-to-End Imitation Learning cites this paper.
State-Conditional Adversarial Learning: An Off-Policy Visual Domain Transfer Method for End-to-End Imitation Learning Using Simulation and Domain Adaptation to Improve Efficiency of Deep Robotic Grasping
Reference 25
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-05-21T18:24:18.211803Z
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 26024416-eafe-4743-86b3-e614f25977ec · inbound
State-Conditional Adversarial Learning: An Off-Policy Visual Domain Transfer Method for End-to-End Imitation Learning cites this paper.
State-Conditional Adversarial Learning: An Off-Policy Visual Domain Transfer Method for End-to-End Imitation Learning Using Simulation and Domain Adaptation to Improve Efficiency of Deep Robotic Grasping
Reference 23
- Resolution
- unresolved
- no resolver link, observed 2026-08-03T18:30:49.411746Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.