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Paper Citation Record · LEDGER

Prior Reinforce: Goal-Conditioned Dynamic Manipulation with Limited Trials

As of 8 August 2026, this Paper Citation Record lists 39 of 39 outbound references and 0 inbound Pith citation observations for arXiv:2505.21916.

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

pith.paper-citation-record.v1
2505.21916 v3

Coverage vector

measured 39 of 39 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:26:30.721159Z

measured 39 of 39 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+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 exact2
  • verified fuzzy21
  • unresolved16
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation dde0c067-a19c-4bb4-a90a-fc4a2df9b1dc · outbound

This paper cites $\pi_0$: A Vision-Language-Action Flow Model for General Robot Control.

Prior Reinforce: Goal-Conditioned Dynamic Manipulation with Limited Trials $\pi_0$: A Vision-Language-Action Flow Model for General Robot Control

Reference 1

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source=pdf_text observed=2026-08-07T13:26:26.933507Z digest=sha256:42b1849328969c1a314f4da1b1b4712342e8457f2b97bacfb35e84c9fcd1530b

Observation 9f6e0cc4-f8e1-4751-be15-928630f409cb · outbound

This paper cites Catch it! learning to catch in flight with mobile dexterous hands,.

Prior Reinforce: Goal-Conditioned Dynamic Manipulation with Limited Trials Catch it! learning to catch in flight with mobile dexterous hands,

Reference 2

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T13:26:27.044744Z digest=sha256:513f651f2072849416063c8a83b0ea18901a69783bd6c17c02a0ea20b384830d

Observation 1841f679-70c0-43e7-bdd4-f68484d81443 · outbound

This paper cites Reinforcement Learning with Foundation Priors: Let the Embodied Agent Efficiently Learn on Its Own.

Prior Reinforce: Goal-Conditioned Dynamic Manipulation with Limited Trials Reinforcement Learning with Foundation Priors: Let the Embodied Agent Efficiently Learn on Its Own

Reference 3

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source=pdf_text observed=2026-08-07T13:26:27.138792Z digest=sha256:f5d8c2d886a5336336209154821156f0d2c329127baa1519d52036629a133c7b

Observation 1abd5754-45bb-45f9-8ccb-57100baebb7b · outbound

This paper cites Roboagent: Generalization and efficiency in robot manipulation via semantic augmentations and action chunking,.

Prior Reinforce: Goal-Conditioned Dynamic Manipulation with Limited Trials Roboagent: Generalization and efficiency in robot manipulation via semantic augmentations and action chunking,

Reference 4

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T13:26:27.219525Z digest=sha256:f655cdfc5f5ccba7caba3939b2f22ee684640ecf3c22be71c9b578479a688f8f

Observation d2e3e260-18f9-4865-bca9-c236acf5b96f · outbound

This paper cites Diffusion policy: Visuomotor policy learning via ac- tion diffusion,.

Prior Reinforce: Goal-Conditioned Dynamic Manipulation with Limited Trials Diffusion policy: Visuomotor policy learning via ac- tion diffusion,

Reference 5

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source=pdf_text observed=2026-08-07T13:26:27.288599Z digest=sha256:8a51758668a23e3258ebb639d6362080fc3cade650c6a05858c7574c60e0875c

Observation 1a818a0e-9eb9-4d24-bcb1-cc8277653bf5 · outbound

This paper cites Universal Manipulation Interface: In-The-Wild Robot Teaching Without In-The-Wild Robots.

Prior Reinforce: Goal-Conditioned Dynamic Manipulation with Limited Trials Universal Manipulation Interface: In-The-Wild Robot Teaching Without In-The-Wild Robots

Reference 6

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source=pdf_text observed=2026-08-07T13:26:27.372085Z digest=sha256:308f99ea84d60845c83fab9579d3217020e6c73e832ee081fd77623a69c1e498

Observation 3d9c947f-18ab-4d0e-8904-b337e9c1468b · outbound

This paper cites UMI on legs: Making manipulation policies mobile with manipulation-centric whole-body controllers,.

Prior Reinforce: Goal-Conditioned Dynamic Manipulation with Limited Trials UMI on legs: Making manipulation policies mobile with manipulation-centric whole-body controllers,

Reference 7

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T13:26:27.448258Z digest=sha256:22b8d03f2e4a3a40a9f16f10eb30c649c5dd107a57070749324c888f59d75949

Observation 9b125b66-afd3-4529-b500-dbfee911de7b · outbound

This paper cites SKIL: Semantic Keypoint Imitation Learning for Generalizable Data-efficient Manipulation.

Prior Reinforce: Goal-Conditioned Dynamic Manipulation with Limited Trials SKIL: Semantic Keypoint Imitation Learning for Generalizable Data-efficient Manipulation

Reference 8

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source=pdf_text observed=2026-08-07T13:26:27.535567Z digest=sha256:66a7593afda15bfae2830436ef3e4544051148920d917a87782e14f623f0a385

Observation 93f944c3-9a32-465c-8dd2-7b02c676f962 · outbound

This paper cites Rt-1: Robotics transformer for real-world control at scale,.

Prior Reinforce: Goal-Conditioned Dynamic Manipulation with Limited Trials Rt-1: Robotics transformer for real-world control at scale,

Reference 9

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T13:26:27.626948Z digest=sha256:ac38c156e526bdce348c2bc942549953e3e7ed43d277bcc9bd97cc2b8f85948f

Observation 566b6b29-ce8c-4803-8fd9-43d9c06e3977 · outbound

This paper cites Openvla: An open-source vision-language-action model,.

Prior Reinforce: Goal-Conditioned Dynamic Manipulation with Limited Trials Openvla: An open-source vision-language-action model,

Reference 10

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T13:26:27.705369Z digest=sha256:91d9a705ac8f6afecc1b7eb779bb6a2c68cb0ea326cd63d645c7e59143b9fcfa

Observation 9c99f3bd-bee6-41bf-8229-358bde098955 · outbound

This paper cites RDT-1B: a Diffusion Foundation Model for Bimanual Manipulation.

Prior Reinforce: Goal-Conditioned Dynamic Manipulation with Limited Trials RDT-1B: a Diffusion Foundation Model for Bimanual Manipulation

Reference 11

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source=pdf_text observed=2026-08-07T13:26:27.782344Z digest=sha256:32c05af450bd98703478a64dae592f6d4f0cc90230071da035717c26e4cf5191

Observation 3d051b29-32ce-4515-b48c-61b8d6100d8d · outbound

This paper cites Copa: General robotic manipulation through spatial constraints of parts with foundation models,.

Prior Reinforce: Goal-Conditioned Dynamic Manipulation with Limited Trials Copa: General robotic manipulation through spatial constraints of parts with foundation models,

Reference 12

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T13:26:27.870762Z digest=sha256:d09801556884bf67c5426eb959d41ace09ea10d18f9d156a2edc9a8bc439bef4

Observation 1c315c49-60a8-468f-861c-1766ead52a0b · outbound

This paper cites Onetwovla: A unified vision-language-action model with adaptive reasoning,.

Prior Reinforce: Goal-Conditioned Dynamic Manipulation with Limited Trials Onetwovla: A unified vision-language-action model with adaptive reasoning,

Reference 13

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:26:27.988402Z digest=sha256:eeda2984bd87d27605a93e98e099111c2ea3034113e777c31c8c890c50878bfe

Observation 3b0f671b-579c-4dbe-a7d7-2a76569d55c1 · outbound

This paper cites Tactile-VLA: Unlocking Vision-Language-Action Model's Physical Knowledge for Tactile Generalization.

Prior Reinforce: Goal-Conditioned Dynamic Manipulation with Limited Trials Tactile-VLA: Unlocking Vision-Language-Action Model's Physical Knowledge for Tactile Generalization

Reference 14

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source=pdf_text observed=2026-08-07T13:26:28.067832Z digest=sha256:475a009bbdf0f1d673d271e2beb3a515554e713263c797fad15572236796ee7a

Observation 54865958-4082-4bd8-b9fe-de647e0e243e · outbound

This paper cites Itera- tive residual policy: for goal-conditioned dynamic manipulation of deformable objects,.

Prior Reinforce: Goal-Conditioned Dynamic Manipulation with Limited Trials Itera- tive residual policy: for goal-conditioned dynamic manipulation of deformable objects,

Reference 15

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T13:26:28.167940Z digest=sha256:df99fa40e576a4d16316f9b2b6c123cf1ce444b9526c9676954ae369b88afb03

Observation 409f3c03-2e24-4a06-b4bb-276049887685 · outbound

This paper cites Dynamic handover: Throw and catch with bimanual hands,.

Prior Reinforce: Goal-Conditioned Dynamic Manipulation with Limited Trials Dynamic handover: Throw and catch with bimanual hands,

Reference 16

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T13:26:28.264807Z digest=sha256:307870052125a55f5ec52b05895f4a7f12552d6f50f687f0d0b54ee48756a6f1

Observation 0940180c-58d3-43f0-977a-7290a7d4d1ff · outbound

This paper cites Tossing- bot: Learning to throw arbitrary objects with residual physics,.

Prior Reinforce: Goal-Conditioned Dynamic Manipulation with Limited Trials Tossing- bot: Learning to throw arbitrary objects with residual physics,

Reference 17

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source=pdf_text observed=2026-08-07T13:26:28.365732Z digest=sha256:2d29a8271edf36deb6a86b8588d61853aa7c3bb91471f45b127c260a97627425

Observation ed05babb-c3e0-49b7-a7b3-7bd603461c4a · outbound

This paper cites A stochastic dynamic motion planning al- gorithm for object-throwing,.

Prior Reinforce: Goal-Conditioned Dynamic Manipulation with Limited Trials A stochastic dynamic motion planning al- gorithm for object-throwing,

Reference 18

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T13:26:28.466750Z digest=sha256:718301251fc6bf0ee731acd7c233051dbfdbe6269957ee847b8d47f47dd981c1

Observation 41f110a7-93d0-4e8e-9ed7-82de4746097a · outbound

This paper cites Optimal shape and motion planning for dynamic planar manipulation,.

Prior Reinforce: Goal-Conditioned Dynamic Manipulation with Limited Trials Optimal shape and motion planning for dynamic planar manipulation,

Reference 19

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T13:26:28.614868Z digest=sha256:838182283060ee6be724756873bb58d846eaae373652c839fe4c63517dbef84d

Observation e30fe29a-1bf1-4fcf-9ecd-486fb5b5cd6e · outbound

This paper cites Learning agile robotic locomotion skills by imitating animals,.

Prior Reinforce: Goal-Conditioned Dynamic Manipulation with Limited Trials Learning agile robotic locomotion skills by imitating animals,

Reference 20

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T13:26:28.756928Z digest=sha256:46eee2d464b508189b3535e1a18ff419ef2a600d3b21a2be92853d5595c6ff66

Observation ab0143bf-26f3-4fc1-83fa-bcf95e4a42c8 · outbound

This paper cites Tool-as-interface: Learning robot policies from human tool usage through imitation learning,.

Prior Reinforce: Goal-Conditioned Dynamic Manipulation with Limited Trials Tool-as-interface: Learning robot policies from human tool usage through imitation learning,

Reference 21

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:26:28.851555Z digest=sha256:55a1c7b41d6ce1ec8c6341f4c3a43cdbb1e44e54e28116ccf4bb24005511bf7a

Observation 2c6da33a-1826-4123-8f81-98d0b68c35a3 · outbound

This paper cites Conditional Variational Auto Encoder Based Dynamic Motion for Multi-task Imitation Learning.

Prior Reinforce: Goal-Conditioned Dynamic Manipulation with Limited Trials Conditional Variational Auto Encoder Based Dynamic Motion for Multi-task Imitation Learning

Reference 22

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T13:26:28.970885Z digest=sha256:3bb732cf217dbb4ae056b629d22ddcd5b4cb76418ccf5e9be8c0d09f080e2044

Observation 654d17d7-a9b9-44b9-9e33-e6b0efd39dbd · outbound

This paper cites Whole-Body Dynamic Throwing with Legged Manipulators.

Prior Reinforce: Goal-Conditioned Dynamic Manipulation with Limited Trials Whole-Body Dynamic Throwing with Legged Manipulators

Reference 23

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local_arxiv, observed 2026-08-07T13:26:31.122942Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T13:26:29.051887Z digest=sha256:b0970ec504c7fadb10809cc355c02080c2ee5a1c54c81e4cb70cc3259cb588c7

Observation cc0130f0-e586-4643-85ed-78d7cda9fac6 · outbound

This paper cites Mockus, V.

Prior Reinforce: Goal-Conditioned Dynamic Manipulation with Limited Trials Mockus, V

Reference 24

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T13:26:29.136403Z digest=sha256:a4fc686748cd7c44d44df255a0503097412cd9a2635f1a6ae34ccf36b0527e70

Observation 1159557e-02ba-4e8c-9fb3-29f6535b7d1e · outbound

This paper cites Gaussian processes in machine learning,.

Prior Reinforce: Goal-Conditioned Dynamic Manipulation with Limited Trials Gaussian processes in machine learning,

Reference 25

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T13:26:29.223928Z digest=sha256:a497bd3cd137ba63070626fdeab687e50bec75a0d95599e26069255d7b7e92fd

Observation fe1c5f1b-6392-4471-87cd-2ab77dfa0faa · outbound

This paper cites A ball-throwing robot with visual feedback,.

Prior Reinforce: Goal-Conditioned Dynamic Manipulation with Limited Trials A ball-throwing robot with visual feedback,

Reference 26

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T13:26:29.308002Z digest=sha256:41b6b238809f73d51e9ea78156d945e911517144797d51891b723a12e2a1cf40

Observation c272b6ad-0489-4fa3-be30-1fb439c7e960 · outbound

This paper cites Dynamic task execution using active parameter identification with the baxter research robot,.

Prior Reinforce: Goal-Conditioned Dynamic Manipulation with Limited Trials Dynamic task execution using active parameter identification with the baxter research robot,

Reference 27

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T13:26:29.455825Z digest=sha256:5545f723b5e344e28ed6a5d9bf8f66432e6649a41a9c25b1ebae874268252def

Observation e46e3e95-2ae3-419d-ab66-f82cc7c1f180 · outbound

This paper cites Dynamic movement primitives in robotics: A tutorial survey,.

Prior Reinforce: Goal-Conditioned Dynamic Manipulation with Limited Trials Dynamic movement primitives in robotics: A tutorial survey,

Reference 28

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:26:29.571907Z digest=sha256:b62f791446210d255ffa601a17704fb1431bcc7d3a6765a2c8c0d4aef616ae4d

Observation ee8585f3-7a79-47e3-997e-c45826390cd3 · outbound

This paper cites Probabilistic movement primitives,.

Prior Reinforce: Goal-Conditioned Dynamic Manipulation with Limited Trials Probabilistic movement primitives,

Reference 29

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T13:26:29.656775Z digest=sha256:f6f896e254ff0661c68917c041f8b190cea91ed8cc7e7af4ca0c223f51411daf

Observation 828c0b84-50c8-4611-b4e0-f7cdfde5a8bf · outbound

This paper cites Prodmp: A unified perspective on dynamic and probabilistic move- ment primitives,.

Prior Reinforce: Goal-Conditioned Dynamic Manipulation with Limited Trials Prodmp: A unified perspective on dynamic and probabilistic move- ment primitives,

Reference 30

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:26:29.794186Z digest=sha256:ef0f4b6cc89e4ef526a6d6621a5da21984df53725ef2b73d448d07e9d5aec3a9

Observation ca74b015-19e8-4212-848f-115f4d5c7298 · outbound

This paper cites Learning table tennis with a mixture of motor primitives,.

Prior Reinforce: Goal-Conditioned Dynamic Manipulation with Limited Trials Learning table tennis with a mixture of motor primitives,

Reference 31

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raw_fallback, observed 2026-08-07T13:26:32.727050Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T13:26:29.941186Z digest=sha256:d7d40ca4b183c4f36bb93034721466cd9fe503b47a78e03ab85d50c21e530fae

Observation f84174ff-b5d3-4a09-98f8-cf654d3b4dfa · outbound

This paper cites Residual learning from demonstration: Adapting dmps for contact-rich manipulation,.

Prior Reinforce: Goal-Conditioned Dynamic Manipulation with Limited Trials Residual learning from demonstration: Adapting dmps for contact-rich manipulation,

Reference 32

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T13:26:30.058492Z digest=sha256:a3f7bab00bcef5972e0ea1acfb399da7146f1d374bc84419415cb5051b7a4afe

Observation 7bd44fb3-2d90-4ab9-b78a-53a7556d8720 · outbound

This paper cites Residual Robot Learning for Object-Centric Probabilistic Movement Primitives.

Prior Reinforce: Goal-Conditioned Dynamic Manipulation with Limited Trials Residual Robot Learning for Object-Centric Probabilistic Movement Primitives

Reference 33

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:26:30.165854Z digest=sha256:56b32685ad1579c2a2bf9dfec613871e577e24da4696ee9fc0d0e7a43cec696b

Observation a33ad91e-5220-4e9c-acb6-1e5527efa933 · outbound

This paper cites Optimizing robot striking movement primitives with iterative learning control,.

Prior Reinforce: Goal-Conditioned Dynamic Manipulation with Limited Trials Optimizing robot striking movement primitives with iterative learning control,

Reference 34

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raw_fallback, observed 2026-08-07T13:26:32.249735Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T13:26:30.241146Z digest=sha256:e700c9f1a692ea45d9045c58f16ec9a30c7d9fc970e3daad2d9a6f777fb8c2b3

Observation c58ef950-819b-49cc-8cb6-ace9411fdcde · outbound

This paper cites HuB: Learning Extreme Humanoid Balance.

Prior Reinforce: Goal-Conditioned Dynamic Manipulation with Limited Trials HuB: Learning Extreme Humanoid Balance

Reference 35

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unresolved
no resolver link, observed 2026-08-07T13:26:30.356430Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:26:30.356430Z digest=sha256:b03727b17c801f57cf79f79ea23f7af6f0c25ab4a26e8254dee6cbf888181c92

Observation bb115905-d894-472a-80c0-0855f0f53da1 · outbound

This paper cites Serl: A software suite for sample- efficient robotic reinforcement learning,.

Prior Reinforce: Goal-Conditioned Dynamic Manipulation with Limited Trials Serl: A software suite for sample- efficient robotic reinforcement learning,

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:26:32.053382Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T13:26:30.444239Z digest=sha256:c24f6e54c18630bd160720deef3f43cb8a2f41235997a6ea3cef55c86f414726

Observation 216a3b5f-2cca-4dda-a44d-aae64872c4a7 · outbound

This paper cites Precise and Dexterous Robotic Manipulation via Human-in-the-Loop Reinforcement Learning.

Prior Reinforce: Goal-Conditioned Dynamic Manipulation with Limited Trials Precise and Dexterous Robotic Manipulation via Human-in-the-Loop Reinforcement Learning

Reference 37

Resolution
unresolved
no resolver link, observed 2026-08-07T13:26:30.534038Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:26:30.534038Z digest=sha256:ca1b2cc61c25a2725949410a452a63d225a52f4890a694db02d212db48fd9532

Observation 6deab804-ea55-4a5f-b512-96f082ec0cb2 · outbound

This paper cites Asap: Aligning simulation and real-world physics for learning agile humanoid whole-body skills,.

Prior Reinforce: Goal-Conditioned Dynamic Manipulation with Limited Trials Asap: Aligning simulation and real-world physics for learning agile humanoid whole-body skills,

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T13:26:31.847434Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T13:26:30.680147Z digest=sha256:1741abdda74c8b35eafa962dbc5c511ab15973d78473f91ccbb8e23fce64b646

Observation 6f8bc91c-4f66-4844-b669-7a3a93dcb7f4 · outbound

This paper cites GLM-4.5V and GLM-4.1V-Thinking: Towards Versatile Multimodal Reasoning with Scalable Reinforcement Learning.

Prior Reinforce: Goal-Conditioned Dynamic Manipulation with Limited Trials GLM-4.5V and GLM-4.1V-Thinking: Towards Versatile Multimodal Reasoning with Scalable Reinforcement Learning

Reference 39

Resolution
unresolved
no resolver link, observed 2026-08-07T13:26:30.721159Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:26:30.721159Z digest=sha256:d9fbd721aa37689f2d646cb279dcf76989a9bd06fad8f34355fc7536cdcc7d80

Pith citing papers

No inbound Pith citation observations are available.