Paper Citation Record · LEDGER

The Ingredients of Real-World Robotic Reinforcement Learning

As of 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 17 inbound Pith citation observations for arXiv:2004.12570.

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

pith.paper-citation-record.v1
2004.12570 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

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 17 of 17 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T12:33:47.319699Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T09:59:44.626163Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
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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 3c0fb13f-2a7f-4cd3-ae6c-34f461c50e48 · inbound

An Empirical Study of Deep Reinforcement Learning in Continuing Tasks cites this paper.

An Empirical Study of Deep Reinforcement Learning in Continuing Tasks The Ingredients of Real-World Robotic Reinforcement Learning

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-10T20:56:51.133757Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1fc3d64b-cddb-42dd-8f93-1b7d4114c1fb · inbound

Flow-based Domain Randomization for Learning and Sequencing Robotic Skills cites this paper.

Flow-based Domain Randomization for Learning and Sequencing Robotic Skills The Ingredients of Real-World Robotic Reinforcement Learning

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-09T14:30:08.919449Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 15a22a07-8bcb-41c6-bbda-546e572d6ced · inbound

Crossing the Human-Robot Embodiment Gap with Sim-to-Real RL using One Human Demonstration cites this paper.

Crossing the Human-Robot Embodiment Gap with Sim-to-Real RL using One Human Demonstration The Ingredients of Real-World Robotic Reinforcement Learning

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-16T12:33:47.319699Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7b83022a-a003-4080-a99f-2f33b4a44360 · inbound

VLA-RL: Towards Masterful and General Robotic Manipulation with Scalable Reinforcement Learning cites this paper.

VLA-RL: Towards Masterful and General Robotic Manipulation with Scalable Reinforcement Learning The Ingredients of Real-World Robotic Reinforcement Learning

Reference 91

Resolution: verified exact; arxiv_id, observed 2026-05-16T12:55:40.405729Z

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 e679ad12-e1af-41e9-a591-c6726325a71e · inbound

CARoL: Context-aware Adaptation for Robot Learning cites this paper.

CARoL: Context-aware Adaptation for Robot Learning The Ingredients of Real-World Robotic Reinforcement Learning

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-07T05:49:54.928309Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ce0ac35c-94a5-48b9-b3fa-d6b9e18f8096 · inbound

SafeMimic: Towards Safe and Autonomous Human-to-Robot Imitation for Mobile Manipulation cites this paper.

SafeMimic: Towards Safe and Autonomous Human-to-Robot Imitation for Mobile Manipulation The Ingredients of Real-World Robotic Reinforcement Learning

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-06T23:55:12.511151Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d0d5d731-8015-4ce6-a8e5-88b4ac8a5704 · inbound

Residual Reward Models for Preference-based Reinforcement Learning cites this paper.

Residual Reward Models for Preference-based Reinforcement Learning The Ingredients of Real-World Robotic Reinforcement Learning

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-06T21:17:36.076495Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 33ee60cd-a81e-48d3-8c09-ebdd61e8a0d1 · inbound

SimLauncher: Launching Sample-Efficient Real-world Robotic Reinforcement Learning via Simulation Pre-training cites this paper.

SimLauncher: Launching Sample-Efficient Real-world Robotic Reinforcement Learning via Simulation Pre-training The Ingredients of Real-World Robotic Reinforcement Learning

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-06T19:53:06.317839Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e31bd30f-f515-41b1-90a0-0c01d78464e3 · inbound

Geometry of Neural Reinforcement Learning in Continuous State and Action Spaces cites this paper.

Geometry of Neural Reinforcement Learning in Continuous State and Action Spaces The Ingredients of Real-World Robotic Reinforcement Learning

Reference 139

Resolution: unresolved; no resolver link, observed 2026-08-06T13:21:59.449740Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation adb13b4b-71bd-4495-b751-9bdfe8dba9c1 · inbound

$\pi^{*}_{0.6}$: a VLA That Learns From Experience cites this paper.

$\pi^{*}_{0.6}$: a VLA That Learns From Experience The Ingredients of Real-World Robotic Reinforcement Learning

Reference 85

Resolution: verified exact; arxiv_id, observed 2026-05-12T10:34:59.439726Z

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 71344456-bc8b-40c8-9b23-eaf0b07ed8ab · inbound

Auto-exploration for online reinforcement learning cites this paper.

Auto-exploration for online reinforcement learning The Ingredients of Real-World Robotic Reinforcement Learning

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-03T18:19:01.453963Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4796b188-2383-420a-abcf-946678d6cf8f · inbound

Auto-exploration for online reinforcement learning cites this paper.

Auto-exploration for online reinforcement learning The Ingredients of Real-World Robotic Reinforcement Learning

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-04T06:51:04.150269Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3ef674cb-e39e-49f4-ade1-ec2e4348e9e2 · inbound

From Prior to Pro: Efficient Skill Mastery via Distribution Contractive RL Finetuning cites this paper.

From Prior to Pro: Efficient Skill Mastery via Distribution Contractive RL Finetuning The Ingredients of Real-World Robotic Reinforcement Learning

Reference 35

Resolution: unresolved; no resolver link, observed 2026-07-14T23:46:32.301737Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5c86aa38-7478-473e-a601-fdf79fc51a6d · inbound

RL Token: Bootstrapping Online RL with Vision-Language-Action Models cites this paper.

RL Token: Bootstrapping Online RL with Vision-Language-Action Models The Ingredients of Real-World Robotic Reinforcement Learning

Reference 26

Resolution: verified exact; arxiv_id, observed 2026-05-11T19:26:09.269122Z

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 534b0b5b-e3b5-4285-8ce5-91dbb54b8b8b · inbound

AllDayNav: Lifelong Navigation via Real-World Reinforcement Learning cites this paper.

AllDayNav: Lifelong Navigation via Real-World Reinforcement Learning The Ingredients of Real-World Robotic Reinforcement Learning

Reference 60

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T05:07:38.984009Z

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 0cbc1fb2-9f07-4fa9-963b-18f5808bb34a · inbound

UniIntervene: Agentic Intervention for Efficient Real-World Reinforcement Learning cites this paper.

UniIntervene: Agentic Intervention for Efficient Real-World Reinforcement Learning The Ingredients of Real-World Robotic Reinforcement Learning

Reference 49

Resolution: verified exact; arxiv_id, observed 2026-07-03T10:58:02.679820Z

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 a877d542-c656-4cad-b672-20f8ee46bd91 · inbound

Learning Process Rewards via Success Visitation Matching for Efficient RL cites this paper.

Learning Process Rewards via Success Visitation Matching for Efficient RL The Ingredients of Real-World Robotic Reinforcement Learning

Reference 98

Resolution: metadata mismatch; arxiv_id, observed 2026-07-04T09:59:44.627806Z

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.