Paper Citation Record · LEDGER

Learning to Manipulate Anywhere: A Visual Generalizable Framework For Reinforcement Learning

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 11 inbound Pith citation observations for arXiv:2407.15815.

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

pith.paper-citation-record.v1
2407.15815 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 11 of 11 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 11 of 11 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T22:04:34.686270Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T09:19:42.969889Z

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 5fb88f38-08dc-41bf-a731-8ecdce4377e2 · inbound

RoboPearls: Editable Video Simulation for Robot Manipulation cites this paper.

RoboPearls: Editable Video Simulation for Robot Manipulation Learning to Manipulate Anywhere: A Visual Generalizable Framework For Reinforcement Learning

Reference 88

Resolution: unresolved; no resolver link, observed 2026-08-06T22:04:34.686270Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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

DeGuV: Depth-Guided Visual Reinforcement Learning for Generalization and Interpretability in Manipulation cites this paper.

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

Resolution: unresolved; no resolver link, observed 2026-08-05T05:47:31.510415Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation af4cad6e-cb31-41c1-bd65-1c506c350e7c · inbound

SimpleVLA-RL: Scaling VLA Training via Reinforcement Learning cites this paper.

SimpleVLA-RL: Scaling VLA Training via Reinforcement Learning Learning to Manipulate Anywhere: A Visual Generalizable Framework For Reinforcement Learning

Reference 33

Resolution: verified exact; arxiv_id, observed 2026-05-15T08:02:11.434219Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 9b9c7080-d926-47d3-913e-f52d6051a52f · inbound

One Hand to Rule Them All: Canonical Representations for Unified Dexterous Manipulation cites this paper.

One Hand to Rule Them All: Canonical Representations for Unified Dexterous Manipulation Learning to Manipulate Anywhere: A Visual Generalizable Framework For Reinforcement Learning

Reference 40

Resolution: verified exact; arxiv_id, observed 2026-05-21T12:30:07.553096Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 4f1c7726-e860-4e3f-b64e-f548c7943598 · inbound

DockAnywhere: Data-Efficient Visuomotor Policy Learning for Mobile Manipulation via Novel Demonstration Generation cites this paper.

DockAnywhere: Data-Efficient Visuomotor Policy Learning for Mobile Manipulation via Novel Demonstration Generation Learning to Manipulate Anywhere: A Visual Generalizable Framework For Reinforcement Learning

Reference 14

Resolution: verified exact; arxiv_id, observed 2026-05-10T10:19:20.200096Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation b1be1fe7-1c3f-40b0-9138-748b8fb43046 · inbound

3D Generation for Embodied AI and Robotic Simulation: A Survey cites this paper.

3D Generation for Embodied AI and Robotic Simulation: A Survey Learning to Manipulate Anywhere: A Visual Generalizable Framework For Reinforcement Learning

Reference 172

Resolution: verified exact; arxiv_id, observed 2026-05-12T09:01:25.431015Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 6f77de62-09fd-4640-9af4-f709e67b63b0 · inbound

3D Generation for Embodied AI and Robotic Simulation: A Survey cites this paper.

3D Generation for Embodied AI and Robotic Simulation: A Survey Learning to Manipulate Anywhere: A Visual Generalizable Framework For Reinforcement Learning

Reference 172

Resolution: verified exact; arxiv_id, observed 2026-05-11T22:06:13.023187Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation f53ec3fe-5641-462d-a7a3-51fe08ad38f1 · inbound

3D Generation for Embodied AI and Robotic Simulation: A Survey cites this paper.

3D Generation for Embodied AI and Robotic Simulation: A Survey Learning to Manipulate Anywhere: A Visual Generalizable Framework For Reinforcement Learning

Reference 172

Resolution: verified exact; arxiv_id, observed 2026-05-11T04:05:59.046809Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 8cab7d7d-a445-40da-8c25-710cd62bd6c2 · inbound

Scalable Multi-Task Data Generation via Reinforcement Learning for Language-Conditioned Bimanual Dexterous Manipulation cites this paper.

Scalable Multi-Task Data Generation via Reinforcement Learning for Language-Conditioned Bimanual Dexterous Manipulation Learning to Manipulate Anywhere: A Visual Generalizable Framework For Reinforcement Learning

Reference 31

Resolution: verified exact; arxiv_id, observed 2026-07-04T09:19:42.971686Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 37a7f439-aa69-4a6a-be3e-2587b4f14265 · inbound

Scalable Multi-Task Data Generation via Reinforcement Learning for Language-Conditioned Bimanual Dexterous Manipulation cites this paper.

Reference 31

Resolution: verified exact; arxiv_id, observed 2026-06-30T10:54:36.914892Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation ce9bad05-4d09-4e77-8520-05ba9bc6ce06 · inbound

HALO-WA: Hybrid-Attention Latent-Guided Online Reinforcement Learning for World-Action Models cites this paper.

HALO-WA: Hybrid-Attention Latent-Guided Online Reinforcement Learning for World-Action Models Learning to Manipulate Anywhere: A Visual Generalizable Framework For Reinforcement Learning

Reference 8

Resolution: unresolved; no resolver link, observed 2026-07-11T20:32:22.412216Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.