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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 2026-08-07T06:34:17.273281+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

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

source=pdf_text observed=2026-08-07T13:26:27.044744Z digest=sha256:e965062d67049e67967af4067b3eb76ae91f6d8038584c879f7092387e365fb8

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

source=pdf_text observed=2026-08-07T13:26:27.219525Z digest=sha256:a6305ea3ea85163a7b0d72e7ce0b1c332cba82f27bcd86d6a8e42e2680dd9a34

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

source=pdf_text observed=2026-08-07T13:26:27.448258Z digest=sha256:c160f0cd746fc0c2d24a4d5897febc09f1d37df58c47064156e8f1dcfc08ad2d

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

source=pdf_text observed=2026-08-07T13:26:27.626948Z digest=sha256:500f92fd51d8c47a89457a37e0a4dffebd43a1195eef631060f002f27edcff48

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T13:26:27.705369Z digest=sha256:f5740d5a86dbb831e997c7bdd7ad55a529fcb2a6194b2bac59d8f6207c12f02e

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T13:26:27.870762Z digest=sha256:2479000828d56bafb3ff60cec7a9030452d73b91890c2118c5c5439548ecda76

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

source=pdf_text observed=2026-08-07T13:26:28.167940Z digest=sha256:72f5b77d1f4766e78e4be4e6a3acfbda5181564998e59b50461e8d4f10bc6fff

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

source=pdf_text observed=2026-08-07T13:26:28.264807Z digest=sha256:814b9bbeb5e34e899d2c612ab7a9d9ae29ac83a9e55198494791dfcc68fa8729

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

source=pdf_text observed=2026-08-07T13:26:28.466750Z digest=sha256:16ce80deefedd428ebe6b4c9abbbb955ce2496487b5627a47c6a7f5e642959cc

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

source=pdf_text observed=2026-08-07T13:26:28.614868Z digest=sha256:fb45f8c1d3c6ea41eeed33c6ec27917259fe96257452c46debede627042a8855

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

source=pdf_text observed=2026-08-07T13:26:28.756928Z digest=sha256:1631d3d91d887dbda9c106cff0c3d0824c909b8cce78bf537fbb2f82e181d808

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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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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T13:26:28.970885Z digest=sha256:3c17c0e6bcb5faead3bcf8b29cc57eee7bcacd49a7fd0ec0731e7849c6532808

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

source=pdf_text observed=2026-08-07T13:26:29.051887Z digest=sha256:61325a0757b88442527a5a076d0980965538d92c2c2bb8acbdd3edfa38c6945f

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T13:26:29.136403Z digest=sha256:43212d7b50ffe53359a05ea2bfa0602ad3fa25d1183c529baa7290f644d752d7

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T13:26:29.223928Z digest=sha256:b3f4cfb4fd18bdb1adc7a55d5ef22547faac31a1e2f90df0dfa22c8348bea4e6

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T13:26:29.308002Z digest=sha256:bcd005dd1bbfbae9f5d87ec90481fd0ea1761fe6a08690b6adf8a73ee2b5df3e

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

source=pdf_text observed=2026-08-07T13:26:29.455825Z digest=sha256:aac7f70bd7dfdc9666ccec3251c2a74a3864388edb22c1356ce3e1deec63c528

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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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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T13:26:29.656775Z digest=sha256:efcdce57fc526d6641c777b7a93e220b274d30feae68414b53b7b46c0cac0d88

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T13:26:29.941186Z digest=sha256:6a46287dfb367a5f14695facf675406685eb55d555b13c22a71beca039ea97ee

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

source=pdf_text observed=2026-08-07T13:26:30.058492Z digest=sha256:04de609108deb71f34801f43f87223be23f2bc5dcbb60fe1bda26a6b6beb19c1

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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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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T13:26:30.241146Z digest=sha256:bb904a63caae3b24ca7b500bc2e8372f75cf86c07495946ceb8becd2cd910b94

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

Resolution
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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T13:26:30.444239Z digest=sha256:739d33f70b0b242a46bf035a178a9789ac6fce3e1d552aca2c94e10d72d7cb7b

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T13:26:30.680147Z digest=sha256:bbcfc9a883aeb6f6296646538a3e1bab596ec402676a588b05b977fe97711009

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.