Paper Citation Record · LEDGER

One Demonstration Is Enough for Real-World Robotic Reinforcement Learning

As of 7 August 2026, this Paper Citation Record lists 29 of 29 outbound references and 0 inbound Pith citation observations for arXiv:2607.01651.

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

pith.paper-citation-record.v1
2607.01651 v1

Coverage vector

measured 29 of 29 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-03T12:40:11.283716Z

measured 29 of 29 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 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

29 of 29 outbound references displayed

verified exact18
verified fuzzy4
unresolved0
parse uncertain0
malformed identifier1
metadata mismatch6

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation c5dba6b1-5136-41bb-bf5a-955fe37370f3 · outbound

This paper cites Safe Reinforcement Learning via Shielding.

One Demonstration Is Enough for Real-World Robotic Reinforcement Learning Safe Reinforcement Learning via Shielding

Reference 1

Resolution: verified exact; local_arxiv, observed 2026-07-03T12:48:11.099901Z

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 ae239259-5324-4226-b3eb-ab2aeac532bb · outbound

This paper cites ConRFT: A Reinforced Fine-tuning Method for VLA Models via Consistency Policy.

One Demonstration Is Enough for Real-World Robotic Reinforcement Learning ConRFT: A Reinforced Fine-tuning Method for VLA Models via Consistency Policy

Reference 2

Resolution: verified exact; arxiv_id, observed 2026-07-03T12:48:11.139831Z

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 3e54b218-d694-4087-8280-87588666b55e · outbound

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

One Demonstration Is Enough for Real-World Robotic Reinforcement Learning UniIntervene: Agentic Intervention for Efficient Real-World Reinforcement Learning

Reference 3

Resolution: metadata mismatch; local_arxiv, observed 2026-07-03T12:48:11.120391Z

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 096060d8-e486-466b-985f-203fec17463b · outbound

This paper cites Challenges of Real-World Reinforcement Learning.

One Demonstration Is Enough for Real-World Robotic Reinforcement Learning Challenges of Real-World Reinforcement Learning

Reference 4

Resolution: metadata mismatch; local_arxiv, observed 2026-07-03T12:48:11.129886Z

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 85de99bf-ef5d-47f1-944c-3151127b0eb0 · outbound

This paper cites A General Safety Framework for Learning-Based Control in Uncertain Robotic Systems.

One Demonstration Is Enough for Real-World Robotic Reinforcement Learning A General Safety Framework for Learning-Based Control in Uncertain Robotic Systems

Reference 5

Resolution: verified exact; local_arxiv, observed 2026-07-03T12:48:11.134980Z

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 c7df5e04-4c97-4e3e-8c7b-a5e95421d956 · outbound

This paper cites In: 2012 IEEE International Conference on Robotics and Automation.

One Demonstration Is Enough for Real-World Robotic Reinforcement Learning In: 2012 IEEE International Conference on Robotics and Automation

Reference 6

Resolution: malformed identifier; arxiv_id, observed 2026-07-03T12:48:11.132576Z

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 2fc3bb0c-3a6a-4c76-a83c-dba5050bb49e · outbound

This paper cites ThriftyDAgger: Budget-Aware Novelty and Risk Gating for Interactive Imitation Learning.

One Demonstration Is Enough for Real-World Robotic Reinforcement Learning ThriftyDAgger: Budget-Aware Novelty and Risk Gating for Interactive Imitation Learning

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-07-03T12:48:11.112599Z

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 d5dff37c-69d6-4fb0-bded-f9c65388aa07 · outbound

This paper cites Robot Trains Robot: Automatic Real-World Policy Adaptation and Learning for Humanoids.

One Demonstration Is Enough for Real-World Robotic Reinforcement Learning Robot Trains Robot: Automatic Real-World Policy Adaptation and Learning for Humanoids

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-07-03T12:48:11.094715Z

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 db0c5ccd-8033-450b-9350-057f1ecfb93c · outbound

This paper cites Coarse-to-Fine Imitation Learning: Robot Manipulation from a Single Demonstration.

One Demonstration Is Enough for Real-World Robotic Reinforcement Learning Coarse-to-Fine Imitation Learning: Robot Manipulation from a Single Demonstration

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-07-03T12:48:11.088930Z

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 a1302369-e653-4647-8e51-5c4441b1d9ea · outbound

This paper cites HG-DAgger: Interactive Imitation Learning with Human Experts.

One Demonstration Is Enough for Real-World Robotic Reinforcement Learning HG-DAgger: Interactive Imitation Learning with Human Experts

Reference 10

Resolution: verified exact; local_arxiv, observed 2026-07-03T12:48:11.118041Z

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 6b1b0889-0d09-4e2d-9f6d-0851eb82d7bd · outbound

This paper cites Failure-Aware RL: Reliable Offline- to-Online Reinforcement Learning with Self-Recovery for Real-World Manipulation.

One Demonstration Is Enough for Real-World Robotic Reinforcement Learning Failure-Aware RL: Reliable Offline- to-Online Reinforcement Learning with Self-Recovery for Real-World Manipulation

Reference 11

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T12:48:11.086430Z

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 9530e7d5-4547-4625-9f36-119200c0fba9 · outbound

This paper cites Robust Model Predictive Shielding for Safe Reinforcement Learning with Stochastic Dynamics.

One Demonstration Is Enough for Real-World Robotic Reinforcement Learning Robust Model Predictive Shielding for Safe Reinforcement Learning with Stochastic Dynamics

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-07-03T12:48:11.125043Z

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 dac69b05-f6ac-4ef5-8ee6-28e583e21304 · outbound

This paper cites Robot Learning on the Job: Human-in-the-Loop Autonomy and Learning During Deployment.

One Demonstration Is Enough for Real-World Robotic Reinforcement Learning Robot Learning on the Job: Human-in-the-Loop Autonomy and Learning During Deployment

Reference 13

Resolution: verified exact; arxiv_id, observed 2026-07-03T12:48:11.102416Z

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 f1702531-5658-4dec-965f-96ae4d77bfb8 · outbound

This paper cites In: 2024 IEEE International Conference on Robotics and Automation (ICRA).

One Demonstration Is Enough for Real-World Robotic Reinforcement Learning In: 2024 IEEE International Conference on Robotics and Automation (ICRA)

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T08:10:48.217576Z

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 1fe52abe-072e-402f-82e1-73fbb11a7d71 · outbound

This paper cites Science Robotics10(105), eads5033 (2025).

One Demonstration Is Enough for Real-World Robotic Reinforcement Learning Science Robotics10(105), eads5033 (2025)

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T08:10:48.219700Z

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 5ab7515f-1351-4d40-96fd-6ec3b15cd3ca · outbound

This paper cites Human-in-the-Loop Imitation Learning using Remote Teleoperation.

One Demonstration Is Enough for Real-World Robotic Reinforcement Learning Human-in-the-Loop Imitation Learning using Remote Teleoperation

Reference 16

Resolution: verified exact; arxiv_id, observed 2026-07-03T12:48:11.083696Z

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 b19b1f52-e108-4769-9bf9-1e6415382d28 · outbound

This paper cites Playing Atari with Deep Reinforcement Learning.

One Demonstration Is Enough for Real-World Robotic Reinforcement Learning Playing Atari with Deep Reinforcement Learning

Reference 17

Resolution: metadata mismatch; local_arxiv, observed 2026-07-03T12:48:11.122714Z

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 8166d853-2ce5-45c9-abe7-97bd198254a8 · outbound

This paper cites In: 2018 IEEE inter- national conference on robotics and automation (ICRA).

One Demonstration Is Enough for Real-World Robotic Reinforcement Learning In: 2018 IEEE inter- national conference on robotics and automation (ICRA)

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T08:10:48.215720Z

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 2e50b993-2f39-42ec-a407-77de45d7801f · outbound

This paper cites On the Effectiveness of Retrieval, Alignment, and Replay in Manipulation.

One Demonstration Is Enough for Real-World Robotic Reinforcement Learning On the Effectiveness of Retrieval, Alignment, and Replay in Manipulation

Reference 19

Resolution: verified exact; arxiv_id, observed 2026-07-03T12:48:11.137310Z

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 7c9c7f79-8910-4b27-ad04-9e2a2a16c1d4 · outbound

This paper cites MILES: Making Imitation Learning Easy with Self-Supervision.

One Demonstration Is Enough for Real-World Robotic Reinforcement Learning MILES: Making Imitation Learning Easy with Self-Supervision

Reference 20

Resolution: verified exact; arxiv_id, observed 2026-07-03T12:48:11.115768Z

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 c5d82b4a-5915-4ad5-8bcc-9c6bd17d6642 · outbound

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

One Demonstration Is Enough for Real-World Robotic Reinforcement Learning Learning Complex Dexterous Manipulation with Deep Reinforcement Learning and Demonstrations

Reference 21

Resolution: metadata mismatch; local_arxiv, observed 2026-07-03T12:48:11.080750Z

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 ede12d47-9489-456d-ba66-b585af81b2bc · outbound

This paper cites A Reduction of Imitation Learning and Structured Prediction to No-Regret Online Learning.

One Demonstration Is Enough for Real-World Robotic Reinforcement Learning A Reduction of Imitation Learning and Structured Prediction to No-Regret Online Learning

Reference 22

Resolution: verified exact; local_arxiv, observed 2026-07-03T12:48:11.091632Z

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 ea370fb2-420d-4372-8612-6dd5c68aa293 · outbound

This paper cites nature529(7587), 484–489 (2016).

One Demonstration Is Enough for Real-World Robotic Reinforcement Learning nature529(7587), 484–489 (2016)

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T08:10:48.213979Z

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 8aa6e697-ad99-41f7-8fc0-beb82cfccea2 · outbound

This paper cites Recovery RL: Safe Reinforcement Learning with Learned Recovery Zones.

One Demonstration Is Enough for Real-World Robotic Reinforcement Learning Recovery RL: Safe Reinforcement Learning with Learned Recovery Zones

Reference 24

Resolution: verified exact; arxiv_id, observed 2026-07-03T12:48:11.127477Z

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 ba4021e6-6b4c-4841-8475-7954fdbd1f6d · outbound

This paper cites Demonstrate Once, Imitate Immediately (DOME): Learning Visual Servoing for One-Shot Imitation Learning.

One Demonstration Is Enough for Real-World Robotic Reinforcement Learning Demonstrate Once, Imitate Immediately (DOME): Learning Visual Servoing for One-Shot Imitation Learning

Reference 25

Resolution: verified exact; arxiv_id, observed 2026-07-03T12:48:11.097265Z

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 6f13c6ce-3a31-480f-a6a0-41eece112469 · outbound

This paper cites Leveraging Demonstrations for Deep Reinforcement Learning on Robotics Problems with Sparse Rewards.

One Demonstration Is Enough for Real-World Robotic Reinforcement Learning Leveraging Demonstrations for Deep Reinforcement Learning on Robotics Problems with Sparse Rewards

Reference 26

Resolution: metadata mismatch; local_arxiv, observed 2026-07-03T12:48:11.109912Z

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 fb5ba526-330f-46a7-84ea-3b71532a2a24 · outbound

This paper cites You Only Demonstrate Once: Category-Level Manipulation from Single Visual Demonstration.

One Demonstration Is Enough for Real-World Robotic Reinforcement Learning You Only Demonstrate Once: Category-Level Manipulation from Single Visual Demonstration

Reference 27

Resolution: verified exact; arxiv_id, observed 2026-07-03T12:48:11.077820Z

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 893ca77d-8416-498a-91c5-1be6cbe975ae · outbound

This paper cites RoboCopilot: Human-in-the-loop Interactive Imitation Learning for Robot Manipulation.

One Demonstration Is Enough for Real-World Robotic Reinforcement Learning RoboCopilot: Human-in-the-loop Interactive Imitation Learning for Robot Manipulation

Reference 28

Resolution: verified exact; arxiv_id, observed 2026-07-03T12:48:11.107581Z

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 99555941-8933-4777-b467-1fce8618fd4a · outbound

This paper cites Compliant residual dagger: Improving real-world contact-rich manipulation with human corrections.

One Demonstration Is Enough for Real-World Robotic Reinforcement Learning Compliant residual dagger: Improving real-world contact-rich manipulation with human corrections

Reference 29

Resolution: verified exact; arxiv_id, observed 2026-07-03T12:48:11.104951Z

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.

Pith citing papers

No inbound Pith citation observations are available.