Paper Citation Record · LEDGER

Domain-Invariant Per-Frame Feature Extraction for Cross-Domain Imitation Learning with Visual Observations

As of 21 August 2026, this Paper Citation Record lists 39 of 39 outbound references and 0 inbound Pith citation observations for arXiv:2502.02867.

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

pith.paper-citation-record.v1
2502.02867 v2

Coverage vector

measured 39 of 39 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-09T10:55:38.472962Z

measured 39 of 39 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+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

39 of 39 outbound references displayed

verified exact3
verified fuzzy24
unresolved11
parse uncertain0
malformed identifier0
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 3a24a114-a932-443d-8b88-8db50f47517f · outbound

This paper cites an unresolved cited work.

Domain-Invariant Per-Frame Feature Extraction for Cross-Domain Imitation Learning with Visual Observations Unresolved cited work

Reference 1

Resolution: unresolved; raw_fallback, observed 2026-08-09T10:55:39.137422Z

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Observation f4c4f418-f29f-4574-94f2-e6725eeb55ba · outbound

This paper cites 𝒕: 1.56Frame Label: 0.64Sequence Label: 0.88Estimatedreward 𝑹.

Domain-Invariant Per-Frame Feature Extraction for Cross-Domain Imitation Learning with Visual Observations 𝒕: 1.56Frame Label: 0.64Sequence Label: 0.88Estimatedreward 𝑹

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T10:55:38.971488Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation e59fe372-292d-4566-bd12-35a7dd819590 · outbound

This paper cites 𝒕: 1.45FrameLabel: 0.76Sequence Label: 1.00Estimatedreward 𝑹.

Domain-Invariant Per-Frame Feature Extraction for Cross-Domain Imitation Learning with Visual Observations 𝒕: 1.45FrameLabel: 0.76Sequence Label: 1.00Estimatedreward 𝑹

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T10:55:38.990125Z

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Observation d322f51c-278a-4a27-93b3-26bc0a3adcc3 · outbound

This paper cites an unresolved cited work.

Domain-Invariant Per-Frame Feature Extraction for Cross-Domain Imitation Learning with Visual Observations Unresolved cited work

Reference 5

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Observation c9778d1a-d640-47fd-a99f-7063848bc95c · outbound

This paper cites Generative adversarial nets.

Domain-Invariant Per-Frame Feature Extraction for Cross-Domain Imitation Learning with Visual Observations Generative adversarial nets

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T10:55:39.320644Z

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Observation 202370b2-8314-4a4e-ab8c-2b964cbdb42a · outbound

This paper cites Robust imitation learning for mobile manipulator focusing on task-related viewpoints and regions.

Domain-Invariant Per-Frame Feature Extraction for Cross-Domain Imitation Learning with Visual Observations Robust imitation learning for mobile manipulator focusing on task-related viewpoints and regions

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T10:55:39.305243Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 5ab52842-d584-4a78-9060-80e788f0ebbf · outbound

This paper cites Adversar- ial imitation learning from video using a state observer.

Domain-Invariant Per-Frame Feature Extraction for Cross-Domain Imitation Learning with Visual Observations Adversar- ial imitation learning from video using a state observer

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T10:55:39.289768Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation ec6414f9-18e5-4a30-ab6c-1c4715e65e0c · outbound

This paper cites Efficient Exploration via State Marginal Matching.

Domain-Invariant Per-Frame Feature Extraction for Cross-Domain Imitation Learning with Visual Observations Efficient Exploration via State Marginal Matching

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-09T10:55:37.856871Z

Source-reported events for the cited work

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Observation 7e1b6740-1473-4a41-b064-10f8f9ce865e · outbound

This paper cites OIL: Observational Imitation Learning.

Domain-Invariant Per-Frame Feature Extraction for Cross-Domain Imitation Learning with Visual Observations OIL: Observational Imitation Learning

Reference 14

Resolution: verified exact; local_arxiv, observed 2026-08-09T10:55:38.791011Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 4475f500-9ef6-4b71-9e48-ee6ca17c8bae · outbound

This paper cites Imitation from observation: Learning to imitate behaviors from raw video via context translation.

Domain-Invariant Per-Frame Feature Extraction for Cross-Domain Imitation Learning with Visual Observations Imitation from observation: Learning to imitate behaviors from raw video via context translation

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T10:55:39.274183Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 01a14776-0bff-4e56-aba0-c08e6f5247e0 · outbound

This paper cites Versatile offline imitation from observations and examples via reg- ularized state-occupancy matching.

Domain-Invariant Per-Frame Feature Extraction for Cross-Domain Imitation Learning with Visual Observations Versatile offline imitation from observations and examples via reg- ularized state-occupancy matching

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T10:55:39.257936Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation e50dc8c1-9932-483c-9f3d-cfa7102dc244 · outbound

This paper cites Time-contrastive networks: Self-supervised learning from video.

Domain-Invariant Per-Frame Feature Extraction for Cross-Domain Imitation Learning with Visual Observations Time-contrastive networks: Self-supervised learning from video

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T10:55:39.239554Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation ff320b2a-65dc-42d1-90f9-2fc5efa76819 · outbound

This paper cites DeepMind Control Suite.

Domain-Invariant Per-Frame Feature Extraction for Cross-Domain Imitation Learning with Visual Observations DeepMind Control Suite

Reference 20

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Source-reported events for the cited work

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Observation 03101c55-f781-429a-b10e-0f3937d0e1dd · outbound

This paper cites Mujoco: A physics engine for model-based control.

Domain-Invariant Per-Frame Feature Extraction for Cross-Domain Imitation Learning with Visual Observations Mujoco: A physics engine for model-based control

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T10:55:39.205489Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 81670c9f-8dbb-455d-b277-009fe037e494 · outbound

This paper cites Decomposing the generalization gap in imitation learning for visual robotic manipulation.

Domain-Invariant Per-Frame Feature Extraction for Cross-Domain Imitation Learning with Visual Observations Decomposing the generalization gap in imitation learning for visual robotic manipulation

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T10:55:39.189152Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 67b7cf3b-133e-449a-84af-d7653cf8dbec · outbound

This paper cites Empirical Evaluation of Rectified Activations in Convolutional Network.

Domain-Invariant Per-Frame Feature Extraction for Cross-Domain Imitation Learning with Visual Observations Empirical Evaluation of Rectified Activations in Convolutional Network

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-09T10:55:38.123852Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation db99e2f7-4fd5-41ec-b703-e3b107343f22 · outbound

This paper cites Cross domain robot imitation with invariant representa- tion.

Domain-Invariant Per-Frame Feature Extraction for Cross-Domain Imitation Learning with Visual Observations Cross domain robot imitation with invariant representa- tion

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T10:55:39.172328Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 34b83334-f33d-4eed-8a8d-27afa5192dc0 · outbound

This paper cites The GP term enforces Lipschitz continuity on the discriminator, stabilizing adversarial training by mitigating extreme gradients and promoting smooth convergence.

Domain-Invariant Per-Frame Feature Extraction for Cross-Domain Imitation Learning with Visual Observations The GP term enforces Lipschitz continuity on the discriminator, stabilizing adversarial training by mitigating extreme gradients and promoting smooth convergence

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T10:55:39.122646Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation ee39446a-2ee7-4e16-add7-5a2fee00d4a6 · outbound

This paper cites The final output is flattened and passed through a dense layer with 32 units.

Domain-Invariant Per-Frame Feature Extraction for Cross-Domain Imitation Learning with Visual Observations The final output is flattened and passed through a dense layer with 32 units

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T10:55:39.106785Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 7cc48a56-23c3-4fe3-8657-6e9300653500 · outbound

This paper cites Architectural specifications of the proposed networks.

Domain-Invariant Per-Frame Feature Extraction for Cross-Domain Imitation Learning with Visual Observations Architectural specifications of the proposed networks

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T10:55:39.091379Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 41a30f3c-d8bd-4310-a2b6-da2c6bdeb80d · outbound

This paper cites For BSR and BT R, random policies are used for data collection.

Domain-Invariant Per-Frame Feature Extraction for Cross-Domain Imitation Learning with Visual Observations For BSR and BT R, random policies are used for data collection

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T10:55:39.076058Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 9d7c6b69-3794-4740-9d07-e011ec20bd2a · outbound

This paper cites It uses an encoder to extract domain-independent features, a domain discriminator to differentiate domains, and a label discriminator to classify expert and non-expert behaviors.

Domain-Invariant Per-Frame Feature Extraction for Cross-Domain Imitation Learning with Visual Observations It uses an encoder to extract domain-independent features, a domain discriminator to differentiate domains, and a label discriminator to classify expert and non-expert behaviors

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T10:55:39.024984Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 7a9ee9c8-582c-4d88-837e-cc5cea4b59ab · outbound

This paper cites A discriminator generates rewards by distinguishing between expert and learner behaviors.

Domain-Invariant Per-Frame Feature Extraction for Cross-Domain Imitation Learning with Visual Observations A discriminator generates rewards by distinguishing between expert and learner behaviors

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T10:55:39.007362Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 477c83dc-4873-4558-8693-a778bd50f82c · outbound

This paper cites Image resolution for each task was configured to the minimum level required for clear agent distinction, optimizing memory usage while maintaining sufficient visual detail.

Domain-Invariant Per-Frame Feature Extraction for Cross-Domain Imitation Learning with Visual Observations Image resolution for each task was configured to the minimum level required for clear agent distinction, optimizing memory usage while maintaining sufficient visual detail

Reference 200

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T10:55:39.044194Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation e461422e-31c4-489f-8a0f-90f3e70c0734 · outbound

This paper cites The target position is defined in polar coordinates, with r ∈ 0.15, 0.2 and 16 Submission and Formatting Instructions for ICML 2025 φ ∈ 0, π/4, π/2,.

Domain-Invariant Per-Frame Feature Extraction for Cross-Domain Imitation Learning with Visual Observations The target position is defined in polar coordinates, with r ∈ 0.15, 0.2 and 16 Submission and Formatting Instructions for ICML 2025 φ ∈ 0, π/4, π/2,

Reference 1000

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T10:55:39.060840Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 9ce42051-1963-468f-9596-f5c711a3fdb5 · outbound

This paper cites Variational Discriminator Bottleneck: Improving Imitation Learning, Inverse RL, and GANs by Constraining Information Flow.

Domain-Invariant Per-Frame Feature Extraction for Cross-Domain Imitation Learning with Visual Observations Variational Discriminator Bottleneck: Improving Imitation Learning, Inverse RL, and GANs by Constraining Information Flow

Reference 2000

Resolution: unresolved; no resolver link, observed 2026-08-09T10:55:37.957364Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b2d321c7-10ef-4f14-b2fc-7431a7c04537 · outbound

This paper cites Environmental and behavioral imitation for autonomous navigation.

Domain-Invariant Per-Frame Feature Extraction for Cross-Domain Imitation Learning with Visual Observations Environmental and behavioral imitation for autonomous navigation

Reference 2004

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T10:55:39.371959Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 5dea54dc-c55b-4dad-89d3-cb2fa736ae0c · outbound

This paper cites Domain-Robust Visual Imitation Learning with Mutual Information Constraints.

Domain-Invariant Per-Frame Feature Extraction for Cross-Domain Imitation Learning with Visual Observations Domain-Robust Visual Imitation Learning with Mutual Information Constraints

Reference 2006

Resolution: unresolved; no resolver link, observed 2026-08-09T10:55:37.798040Z

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Unavailable: canonical work link unavailable.

Observation 2dad6298-11c5-4a24-8df2-34d6fd2c3572 · outbound

This paper cites 12 Submission and Formatting Instructions for ICML 2025 A.

Domain-Invariant Per-Frame Feature Extraction for Cross-Domain Imitation Learning with Visual Observations 12 Submission and Formatting Instructions for ICML 2025 A

Reference 2008

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T10:55:39.153783Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 857a91ba-fd97-44fe-9786-98e056fd9252 · outbound

This paper cites Generative Adversarial Imitation from Observation.

Domain-Invariant Per-Frame Feature Extraction for Cross-Domain Imitation Learning with Visual Observations Generative Adversarial Imitation from Observation

Reference 2012

Resolution: unresolved; no resolver link, observed 2026-08-09T10:55:38.113522Z

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Unavailable: canonical work link unavailable.

Observation fc12cce3-df75-4948-a326-fdc09623b56c · outbound

This paper cites Soft Actor-Critic Algorithms and Applications.

Domain-Invariant Per-Frame Feature Extraction for Cross-Domain Imitation Learning with Visual Observations Soft Actor-Critic Algorithms and Applications

Reference 2014

Resolution: unresolved; no resolver link, observed 2026-08-09T10:55:37.839943Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b261aca9-70db-445b-85dc-74fe6a2d4383 · outbound

This paper cites Offline Imitation from Observation via Primal Wasserstein State Occupancy Matching.

Domain-Invariant Per-Frame Feature Extraction for Cross-Domain Imitation Learning with Visual Observations Offline Imitation from Observation via Primal Wasserstein State Occupancy Matching

Reference 2015

Resolution: metadata mismatch; local_arxiv, observed 2026-08-09T10:55:38.583360Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 89bd068f-0d54-4838-8cc2-3d16ee3965e0 · outbound

This paper cites Learning robust rewards with adverserial inverse reinforcement learning.

Domain-Invariant Per-Frame Feature Extraction for Cross-Domain Imitation Learning with Visual Observations Learning robust rewards with adverserial inverse reinforcement learning

Reference 2017

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T10:55:39.336641Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 23c6bf2d-f2ac-4ee4-9058-f775440287d5 · outbound

This paper cites Imitation Learning from Observations under Transition Model Disparity.

Domain-Invariant Per-Frame Feature Extraction for Cross-Domain Imitation Learning with Visual Observations Imitation Learning from Observations under Transition Model Disparity

Reference 2018

Resolution: verified exact; local_arxiv, observed 2026-08-09T10:55:38.854222Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation fb33711e-681b-448f-85d1-4f7cd149ccda · outbound

This paper cites An integrated frame- work for human–robot collaborative manipulation.

Domain-Invariant Per-Frame Feature Extraction for Cross-Domain Imitation Learning with Visual Observations An integrated frame- work for human–robot collaborative manipulation

Reference 2019

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T10:55:39.221649Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 04589481-37e4-4009-9bfa-d06f1d1ea5db · outbound

This paper cites Model-based inverse reinforcement learning from visual demonstrations.

Domain-Invariant Per-Frame Feature Extraction for Cross-Domain Imitation Learning with Visual Observations Model-based inverse reinforcement learning from visual demonstrations

Reference 2020

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T10:55:39.353869Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 4a6a595b-25fb-4284-8378-f638c9bb7170 · outbound

This paper cites IL-flOw: Imitation Learning from Observation using Normalizing Flows.

Domain-Invariant Per-Frame Feature Extraction for Cross-Domain Imitation Learning with Visual Observations IL-flOw: Imitation Learning from Observation using Normalizing Flows

Reference 2022

Resolution: verified exact; local_arxiv, observed 2026-08-09T10:55:38.917953Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation ea50033a-16b0-48f0-835d-25bd18a7b0ef · outbound

This paper cites Learning Robust Rewards with Adversarial Inverse Reinforcement Learning.

Domain-Invariant Per-Frame Feature Extraction for Cross-Domain Imitation Learning with Visual Observations Learning Robust Rewards with Adversarial Inverse Reinforcement Learning

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-09T10:55:37.819018Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1cce4019-838e-4a4a-9157-2a1dc681cd9f · outbound

This paper cites Primal Wasserstein Imitation Learning.

Domain-Invariant Per-Frame Feature Extraction for Cross-Domain Imitation Learning with Visual Observations Primal Wasserstein Imitation Learning

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-09T10:55:37.808745Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.