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Paper Citation Record · LEDGER

Feature-Based vs. GAN-Based Learning from Demonstrations: When and Why

As of 7 August 2026, this Paper Citation Record lists 26 of 26 outbound references and 1 inbound Pith citation observation for arXiv:2507.05906.

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

pith.paper-citation-record.v1
2507.05906 v2

Coverage vector

measured 26 of 26 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T19:18:54.517768Z

measured 27 of 27 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 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-29T04:34:13.984085Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-29T20:03:57.005841Z

Reference resolution

26 of 26 outbound references displayed

  • verified exact1
  • verified fuzzy6
  • unresolved19
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 78f7d8c8-44a7-44ce-ac2b-74dc7312a657 · outbound

This paper cites GR00T N1: An Open Foundation Model for Generalist Humanoid Robots.

Feature-Based vs. GAN-Based Learning from Demonstrations: When and Why GR00T N1: An Open Foundation Model for Generalist Humanoid Robots

Reference 1

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T19:18:51.926310Z digest=sha256:65a77e22fbc12756cbe950ba94c5cf478a34d8b42173f0d7cd1e908d383ecb3e

Observation 17a8e0bc-e1c3-4c77-a598-6c6d336dd521 · outbound

This paper cites Expressive Whole-Body Control for Humanoid Robots.

Feature-Based vs. GAN-Based Learning from Demonstrations: When and Why Expressive Whole-Body Control for Humanoid Robots

Reference 3

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source=pdf_text observed=2026-08-06T19:18:52.127077Z digest=sha256:ca94aebf1426b4a4c60b52be12541157614f465fd2270db4dc2f25ee9bf9eceb

Observation df15e214-bec4-41c2-b45a-0da2d9be169e · outbound

This paper cites Adversarial motion priors make good substitutes for complex reward functions.

Feature-Based vs. GAN-Based Learning from Demonstrations: When and Why Adversarial motion priors make good substitutes for complex reward functions

Reference 5

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source=pdf_text observed=2026-08-06T19:18:52.402038Z digest=sha256:eb95707315520d9f1f755d22fe9e032667b433030a7e6dd07bf1f991ead2a61e

Observation a86fd55a-70e3-4dcb-a61c-938fda9885e8 · outbound

This paper cites HumanPlus: Humanoid Shadowing and Imitation from Humans.

Feature-Based vs. GAN-Based Learning from Demonstrations: When and Why HumanPlus: Humanoid Shadowing and Imitation from Humans

Reference 7

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source=pdf_text observed=2026-08-06T19:18:52.517566Z digest=sha256:06b4e308266417637e7b2c1fce101901b2acb7c0fd5ae27557afb5b1594a8833

Observation 83c46694-8d4a-42a5-a806-3a1c6b6c48a1 · outbound

This paper cites OmniH2O: Universal and Dexterous Human-to-Humanoid Whole-Body Teleoperation and Learning.

Feature-Based vs. GAN-Based Learning from Demonstrations: When and Why OmniH2O: Universal and Dexterous Human-to-Humanoid Whole-Body Teleoperation and Learning

Reference 9

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source=pdf_text observed=2026-08-06T19:18:52.707507Z digest=sha256:921096ece80dbe2ce076696fe191fec2f07c301ecb9a68f9d238c30871938219

Observation 386d583c-cfbd-4eac-892e-1b69105a76bb · outbound

This paper cites FLD: Fourier Latent Dynamics for Structured Motion Representation and Learning.

Feature-Based vs. GAN-Based Learning from Demonstrations: When and Why FLD: Fourier Latent Dynamics for Structured Motion Representation and Learning

Reference 11

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source=pdf_text observed=2026-08-06T19:18:52.913989Z digest=sha256:0f3cb46b2b54ba9992b95d6ce3662778f5c3f3caab6a827edfd171c034215136

Observation d0a1c02c-54a4-4b6e-b3e0-2f8c7b0f289a · outbound

This paper cites AMO: Adaptive Motion Optimization for Hyper-Dexterous Humanoid Whole-Body Control.

Feature-Based vs. GAN-Based Learning from Demonstrations: When and Why AMO: Adaptive Motion Optimization for Hyper-Dexterous Humanoid Whole-Body Control

Reference 12

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source=pdf_text observed=2026-08-06T19:18:52.976566Z digest=sha256:6c50ff2e169aeb6b9b02b400c01f43aa0efb45e16a236fa02f05e8661ac41782

Observation f8a95298-6000-4976-8098-f134c3f9b472 · outbound

This paper cites Universal Humanoid Motion Representations for Physics-Based Control.

Feature-Based vs. GAN-Based Learning from Demonstrations: When and Why Universal Humanoid Motion Representations for Physics-Based Control

Reference 13

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source=pdf_text observed=2026-08-06T19:18:53.102274Z digest=sha256:0144c535f1a024cd54ba1a75c97dfcfb19a7aaeef8ffb45508a4ff4c2049e1b2

Observation fb0c4137-7f63-4c3f-9755-f84c4c7bb89b · outbound

This paper cites Robot motion diffusion model: Motion generation for robotic characters.

Feature-Based vs. GAN-Based Learning from Demonstrations: When and Why Robot motion diffusion model: Motion generation for robotic characters

Reference 14

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:18:56.072929Z

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.

source=pdf_text observed=2026-08-06T19:18:53.221341Z digest=sha256:ab08b1c905316a73c48b94eb6d8020fd3c9b4f09a44169460eff517a7955b217

Observation 8e70168d-3ef1-41a9-b633-a757ec6dd76a · outbound

This paper cites Humanmimic: Learning natural locomotion and transitions for humanoid robot via wasserstein adversarial imitation.

Feature-Based vs. GAN-Based Learning from Demonstrations: When and Why Humanmimic: Learning natural locomotion and transitions for humanoid robot via wasserstein adversarial imitation

Reference 15

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raw_fallback, observed 2026-08-06T19:18:55.875196Z

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

source=pdf_text observed=2026-08-06T19:18:53.341105Z digest=sha256:7a52bc9aba27c571f301b50380d77a5b695a1058773e7567d673514d5cc1590d

Observation 635b1337-f526-4246-8db4-e683ec4e8f58 · outbound

This paper cites Gemini Robotics: Bringing AI into the Physical World.

Feature-Based vs. GAN-Based Learning from Demonstrations: When and Why Gemini Robotics: Bringing AI into the Physical World

Reference 16

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source=pdf_text observed=2026-08-06T19:18:53.437976Z digest=sha256:d9a714ec78bf00a3c63c2bb26a7c0f22de6eb81b915ba4f7f7e2e3e4dcd538ce

Observation 1131891a-3967-48dc-aa47-f3b112926be7 · outbound

This paper cites Calm: Conditional adversarial latent models for directable virtual characters.

Feature-Based vs. GAN-Based Learning from Demonstrations: When and Why Calm: Conditional adversarial latent models for directable virtual characters

Reference 17

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raw_fallback, observed 2026-08-06T19:18:55.585414Z

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.

source=pdf_text observed=2026-08-06T19:18:53.579784Z digest=sha256:ceb46e0c10fd0de10cacf689cd1688ea73709ac9e829ec930c0ab2d280007953

Observation e980b54b-5e91-43e8-8e55-ac43fd647de6 · outbound

This paper cites Zero-Shot Whole-Body Humanoid Control via Behavioral Foundation Models.

Feature-Based vs. GAN-Based Learning from Demonstrations: When and Why Zero-Shot Whole-Body Humanoid Control via Behavioral Foundation Models

Reference 18

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source=pdf_text observed=2026-08-06T19:18:53.672853Z digest=sha256:ebf752358fcec8a1825f14690fa4b0403098dec869eee46cc55b8ede40718fe2

Observation b0a9d1de-5e81-4f2f-896f-af21b9a32bfa · outbound

This paper cites Advanced skills through multiple adversarial motion priors in reinforcement learning.

Feature-Based vs. GAN-Based Learning from Demonstrations: When and Why Advanced skills through multiple adversarial motion priors in reinforcement learning

Reference 19

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verified fuzzy
raw_fallback, observed 2026-08-06T19:18:55.368665Z

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

source=pdf_text observed=2026-08-06T19:18:53.756313Z digest=sha256:8b3bc8aff64f987a916e5dda6702c826026d781ed96394b7d0054f58b511780d

Observation 955d67a0-f1d1-414c-9066-6923fd04e34d · outbound

This paper cites PhysHOI: Physics-Based Imitation of Dynamic Human-Object Interaction.

Feature-Based vs. GAN-Based Learning from Demonstrations: When and Why PhysHOI: Physics-Based Imitation of Dynamic Human-Object Interaction

Reference 20

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source=pdf_text observed=2026-08-06T19:18:53.874785Z digest=sha256:07f7b1aa4d18ed28c20aa9e19bb1216817568cee420b4be99513ef903c0971f4

Observation 87ee40a5-def8-4657-a763-d19581219892 · outbound

This paper cites DFM: Deep Fourier Mimic for Expressive Dance Motion Learning.

Feature-Based vs. GAN-Based Learning from Demonstrations: When and Why DFM: Deep Fourier Mimic for Expressive Dance Motion Learning

Reference 21

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local_arxiv, observed 2026-08-06T19:18:54.973119Z

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.

source=pdf_text observed=2026-08-06T19:18:53.961622Z digest=sha256:c5fa4bc45a6bd5f52b8759cb8a678608b2ff2cee02d629454a75d8a65a520fbd

Observation 759d5763-dc6d-45e1-8d08-2daf502e5380 · outbound

This paper cites Constrained style learning from imperfect demonstrations under task optimality.

Feature-Based vs. GAN-Based Learning from Demonstrations: When and Why Constrained style learning from imperfect demonstrations under task optimality

Reference 22

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source=pdf_text observed=2026-08-06T19:18:54.083832Z digest=sha256:ab0580a3b9fb4a944455027485c69382caed5a847ebdafd628d7d8cdd04ccb88

Observation 523bbea8-b11d-4035-b11c-210afe24f13f · outbound

This paper cites PARC: Physics-based Augmentation with Reinforcement Learning for Character Controllers.

Feature-Based vs. GAN-Based Learning from Demonstrations: When and Why PARC: Physics-based Augmentation with Reinforcement Learning for Character Controllers

Reference 23

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source=pdf_text observed=2026-08-06T19:18:54.203162Z digest=sha256:572845f1f80e7f526228b844338bfff4f399be9a645e5338ffe4dbcf51a7238a

Observation ff7ad1ec-9aa1-407f-8bae-21ae6514e74a · outbound

This paper cites RobotKeyframing: Learning Locomotion with High-Level Objectives via Mixture of Dense and Sparse Rewards.

Feature-Based vs. GAN-Based Learning from Demonstrations: When and Why RobotKeyframing: Learning Locomotion with High-Level Objectives via Mixture of Dense and Sparse Rewards

Reference 24

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source=pdf_text observed=2026-08-06T19:18:54.280656Z digest=sha256:4150e9429357a48cce278072b864a0282da909e94847c2691218f518a7fd6b76

Observation 40f6f89a-c495-4065-9ffa-008dabe93cef · outbound

This paper cites TWIST: Teleoperated Whole-Body Imitation System.

Feature-Based vs. GAN-Based Learning from Demonstrations: When and Why TWIST: Teleoperated Whole-Body Imitation System

Reference 25

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source=pdf_text observed=2026-08-06T19:18:54.405583Z digest=sha256:773c5af02a1aa5eceb3b6339abe6a1fa8f54b8028a6f494db04cbfb3aad71838

Observation 52d81f56-30d7-45f7-a89f-109813f3f07d · outbound

This paper cites Motion Priors Reimagined: Adapting Flat-Terrain Skills for Complex Quadruped Mobility.

Feature-Based vs. GAN-Based Learning from Demonstrations: When and Why Motion Priors Reimagined: Adapting Flat-Terrain Skills for Complex Quadruped Mobility

Reference 26

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source=pdf_text observed=2026-08-06T19:18:54.517768Z digest=sha256:1bb2f9b977f841b846714b8409afd107388a9f8a0c96d9fd3a86dfb0fd1132fb

Observation 844e5394-9e86-497f-b5c3-3a54fa9a53ed · outbound

This paper cites ExBody2: Advanced Expressive Humanoid Whole-Body Control.

Feature-Based vs. GAN-Based Learning from Demonstrations: When and Why ExBody2: Advanced Expressive Humanoid Whole-Body Control

Reference 2016

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source=pdf_text observed=2026-08-06T19:18:52.764263Z digest=sha256:a7b697a5d9b3d547e621dc471f3ed73ccd42d4859e730b55296114dc9d1b9868

Observation 78f15a55-3cca-411c-a37e-65dca7ea0750 · outbound

This paper cites DreamPolicy: A Unified World-model Policy for Scalable Humanoid Locomotion.

Feature-Based vs. GAN-Based Learning from Demonstrations: When and Why DreamPolicy: A Unified World-model Policy for Scalable Humanoid Locomotion

Reference 2022

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source=pdf_text observed=2026-08-06T19:18:52.486220Z digest=sha256:3a25b4f1d8df345461d8e0d30f25379ba14ad7bc33d215761d3b93c6d0e86cef

Observation 88c0512d-09ef-4fc3-b087-89f5476a4de7 · outbound

This paper cites C · ase: Learning conditional adversarial skill embeddings for physics-based characters.

Feature-Based vs. GAN-Based Learning from Demonstrations: When and Why C · ase: Learning conditional adversarial skill embeddings for physics-based characters

Reference 2023

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verified fuzzy
raw_fallback, observed 2026-08-06T19:18:56.426847Z

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.

source=pdf_text observed=2026-08-06T19:18:52.300885Z digest=sha256:ed166754114e8dee857284e3c12be2157d33a81f6e719f29f5c16c76b54df596

Observation 8024055a-1385-4016-a820-2c43c255c350 · outbound

This paper cites Syn- thesizing physical character-scene interactions.

Feature-Based vs. GAN-Based Learning from Demonstrations: When and Why Syn- thesizing physical character-scene interactions

Reference 2024

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raw_fallback, observed 2026-08-06T19:18:56.250035Z

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.

source=pdf_text observed=2026-08-06T19:18:52.604697Z digest=sha256:83aa64cb22cebd59475c4d2e88243fd8cf8bd7c504c70007fb2968fbe4f2bea7

Observation 2f6e7ff2-e66b-4236-a3b3-f7c4b005d476 · outbound

This paper cites GMT: General Motion Tracking for Humanoid Whole-Body Control.

Feature-Based vs. GAN-Based Learning from Demonstrations: When and Why GMT: General Motion Tracking for Humanoid Whole-Body Control

Reference 2025

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source=pdf_text observed=2026-08-06T19:18:52.011627Z digest=sha256:4f19fd9d955075fd9158a736e25a5eb440802c812b55e4708acfcd059cd898ad

Pith citing papers

Observation 1b17122b-c670-4a0c-beb1-7db4f355df2f · inbound

Booster Lab: A Data-Centric Pipeline for Learning Deployable Humanoid Locomotion Policies cites this paper.

Booster Lab: A Data-Centric Pipeline for Learning Deployable Humanoid Locomotion Policies Feature-Based vs. GAN-Based Learning from Demonstrations: When and Why

Reference 15

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arxiv_id, observed 2026-06-29T20:03:57.010556Z

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.

source=pdf_text observed=2026-06-29T04:34:13.984085Z digest=sha256:5f4df21be89ae8501cc277df385bee8d4146d759d938f4d2e1aec3cde8344e14