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Paper Citation Record · LEDGER

Improving Robotic Imitation Learning via Trajectory Standardization

As of 8 August 2026, this Paper Citation Record lists 25 of 25 outbound references and 0 inbound Pith citation observations for arXiv:2606.22907.

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

pith.paper-citation-record.v1
2606.22907 v1

Coverage vector

measured 25 of 25 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-06-26T08:37:26.864436Z

measured 25 of 25 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

25 of 25 outbound references displayed

  • verified exact3
  • verified fuzzy0
  • unresolved21
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 8bdac25c-b34c-4d14-943d-f0123da50301 · outbound

This paper cites Think Twice, Act Once: Token-Aware Compression and Action Reuse for Efficient Inference in Vision-Language-Action Models.

Improving Robotic Imitation Learning via Trajectory Standardization Think Twice, Act Once: Token-Aware Compression and Action Reuse for Efficient Inference in Vision-Language-Action Models

Reference 1

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arxiv_id, observed 2026-07-04T10:39:45.564349Z

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

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Observation 63ead78d-c225-4817-88f5-170482ca46eb · outbound

This paper cites Learning fine-grained bimanual manipulation with low-cost hardware,.

Improving Robotic Imitation Learning via Trajectory Standardization Learning fine-grained bimanual manipulation with low-cost hardware,

Reference 2

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source=pdf_text observed=2026-06-26T08:37:26.864436Z digest=sha256:e43d712570be034b633f2db40e62f6077431c541260ec79c4de9dfffd238ffac

Observation 2f6f8df9-8e54-4745-b2e6-2694250c21ea · outbound

This paper cites Diffusion Policy: Visuomotor policy learning via action diffusion,.

Improving Robotic Imitation Learning via Trajectory Standardization Diffusion Policy: Visuomotor policy learning via action diffusion,

Reference 3

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source=pdf_text observed=2026-06-26T08:37:26.864436Z digest=sha256:298ed6366771c3a89545ad1f122add0052ccc1ca079edac835d213cac18d01f8

Observation abb53b38-b472-4eee-9818-49a86a0c8866 · outbound

This paper cites RDT-1B: a diffusion foundation model for bimanual ma- nipulation,.

Improving Robotic Imitation Learning via Trajectory Standardization RDT-1B: a diffusion foundation model for bimanual ma- nipulation,

Reference 4

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source=pdf_text observed=2026-06-26T08:37:26.864436Z digest=sha256:24a4c029e90ef74afc913253a2c7fb50359c50b01a7a9c25ac69fd83cf1935da

Observation 6f2ce48b-fb4c-4460-9412-68e33392abeb · outbound

This paper cites CogACT: A Foundational Vision-Language-Action Model for Synergizing Cognition and Action in Robotic Manipulation.

Improving Robotic Imitation Learning via Trajectory Standardization CogACT: A Foundational Vision-Language-Action Model for Synergizing Cognition and Action in Robotic Manipulation

Reference 5

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local_arxiv, observed 2026-07-04T10:39:45.561263Z

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source=pdf_text observed=2026-06-26T08:37:26.864436Z digest=sha256:e40186a35503478a868baf8aa13aba305d072cf1a76fdb8c4e4b2bab17bfa41b

Observation 9cbf7c8a-706f-4cd3-a3b3-8817e1f46363 · outbound

This paper cites FlowPolicy: Enabling fast and robust 3d flow-based policy via consistency flow matching for robot manipulation,.

Improving Robotic Imitation Learning via Trajectory Standardization FlowPolicy: Enabling fast and robust 3d flow-based policy via consistency flow matching for robot manipulation,

Reference 6

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source=pdf_text observed=2026-06-26T08:37:26.864436Z digest=sha256:56e51e43ddbe5f1d3669c1a7c11aafd656599e12dc229496d2262945373044a8

Observation 67a87466-6d75-48cf-bd74-71eefd30d96b · outbound

This paper cites π 0.5: a vision-language-action model with open- world generalization,.

Improving Robotic Imitation Learning via Trajectory Standardization π 0.5: a vision-language-action model with open- world generalization,

Reference 7

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source=pdf_text observed=2026-06-26T08:37:26.864436Z digest=sha256:1fd60a7f89f35c47a8e102843c4463a90291943caebe0bf30c3906888ebd7caa

Observation 1b0e8163-f909-4cfd-ae13-9ef562ccef8d · outbound

This paper cites Fighting copycat agents in behavioral cloning from observation histories,.

Improving Robotic Imitation Learning via Trajectory Standardization Fighting copycat agents in behavioral cloning from observation histories,

Reference 8

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source=pdf_text observed=2026-06-26T08:37:26.864436Z digest=sha256:c12cf3ad7562c1e26d332ecff1a11ca1afee2bce0e3ea05b7a26b5b8924d0d55

Observation 330baecb-fa1a-40a3-80ba-53d1d1455163 · outbound

This paper cites Eliciting compatible demonstrations for multi-human imitation learning,.

Improving Robotic Imitation Learning via Trajectory Standardization Eliciting compatible demonstrations for multi-human imitation learning,

Reference 9

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source=pdf_text observed=2026-06-26T08:37:26.864436Z digest=sha256:fdd65410944f6021f2a06013e3c40f922709a08c6dabd691365634c04d4020a8

Observation af263939-6444-4a84-bc5f-5cc944b61c8b · outbound

This paper cites Towards balanced behavior cloning from imbalanced datasets,.

Improving Robotic Imitation Learning via Trajectory Standardization Towards balanced behavior cloning from imbalanced datasets,

Reference 10

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source=pdf_text observed=2026-06-26T08:37:26.864436Z digest=sha256:48b832ea1727362b40194b63f90879f6fe31eb2cb17369e69e1a22d02c9922b5

Observation 7b257710-3870-4311-9504-eeb261e0b8dd · outbound

This paper cites What matters for adversarial imitation learning?.

Improving Robotic Imitation Learning via Trajectory Standardization What matters for adversarial imitation learning?

Reference 11

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source=pdf_text observed=2026-06-26T08:37:26.864436Z digest=sha256:ba47a22a7ca2790aeb74259e939eb36e6032035cc2aaf1b205298914a2d77fed

Observation 6f3cb0cf-dcf0-41e6-94e5-70d7dcf9a2ed · outbound

This paper cites What matters in learning from offline human demonstrations for robot manipu- lation,.

Improving Robotic Imitation Learning via Trajectory Standardization What matters in learning from offline human demonstrations for robot manipu- lation,

Reference 12

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source=pdf_text observed=2026-06-26T08:37:26.864436Z digest=sha256:f60388e6f4e5eff4a3800eb1cbb27f385fd0b1847ca26fd4e9466b691342d669

Observation 911fb92d-1a46-4782-9635-7838281e1ebf · outbound

This paper cites Vision- language-action models for robotics: A review towards real-world applications,.

Improving Robotic Imitation Learning via Trajectory Standardization Vision- language-action models for robotics: A review towards real-world applications,

Reference 13

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source=pdf_text observed=2026-06-26T08:37:26.864436Z digest=sha256:ed844286aae3be43d4c47a4ba993a51241119b71db42b9a606811e1aed6c6ba5

Observation 60196087-ca34-4bb7-9359-d09d79d085cb · outbound

This paper cites Arc-length- based warping for robot skill synthesis from multiple demonstrations,.

Improving Robotic Imitation Learning via Trajectory Standardization Arc-length- based warping for robot skill synthesis from multiple demonstrations,

Reference 14

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source=pdf_text observed=2026-06-26T08:37:26.864436Z digest=sha256:8f278eb1538623af41cd5ae7794ac0baae9d5cdaba118ad28742b6f1fa62b67a

Observation 9fcf8be7-4302-486f-8e7b-72167762688d · outbound

This paper cites Waypoint-based imitation learning for robotic manipulation,.

Improving Robotic Imitation Learning via Trajectory Standardization Waypoint-based imitation learning for robotic manipulation,

Reference 15

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source=pdf_text observed=2026-06-26T08:37:26.864436Z digest=sha256:3e3952f4eb179b19993aed053df699f85576d023173d0212ec463c1786d8908c

Observation 663bebf4-bb57-4531-b7fe-e5dc7f2e0a38 · outbound

This paper cites Vo-dp: Semantic-geometric adaptive diffusion policy for vision- only robotic manipulation.

Improving Robotic Imitation Learning via Trajectory Standardization Vo-dp: Semantic-geometric adaptive diffusion policy for vision- only robotic manipulation

Reference 16

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arxiv_id, observed 2026-07-04T10:39:45.520277Z

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=2026-06-26T08:37:26.864436Z digest=sha256:038eac4df90f62496f247a84e0c04b1bbbafd1c63dbd8e086eb22a1445ee114d

Observation 66126ab0-dce0-4480-b768-d1be4a1b3f9e · outbound

This paper cites A reduction of imitation learning and structured prediction to no-regret online learning,.

Improving Robotic Imitation Learning via Trajectory Standardization A reduction of imitation learning and structured prediction to no-regret online learning,

Reference 17

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Observation 501ba839-0a5c-4cbe-943a-652c765b665a · outbound

This paper cites HYDRA: Hybrid robot actions for imitation learning,.

Improving Robotic Imitation Learning via Trajectory Standardization HYDRA: Hybrid robot actions for imitation learning,

Reference 18

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source=pdf_text observed=2026-06-26T08:37:26.864436Z digest=sha256:f6b5ac01b7a724be4a0daefe7924de60e1308d0978e50a43c687a8e77f382d4d

Observation fb244fe2-7a60-4ae5-b555-78de81ce80db · outbound

This paper cites View: Visual imitation learning with waypoints,.

Improving Robotic Imitation Learning via Trajectory Standardization View: Visual imitation learning with waypoints,

Reference 19

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Observation cb811a20-4c6c-43d3-a260-e6dd383156ba · outbound

This paper cites Waypoint-based rein- forcement learning for robot manipulation tasks,.

Improving Robotic Imitation Learning via Trajectory Standardization Waypoint-based rein- forcement learning for robot manipulation tasks,

Reference 20

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source=pdf_text observed=2026-06-26T08:37:26.864436Z digest=sha256:699d7ee86be210ef002c4393018f1a744a8c982623f9be9a5198fcd7fdbcd251

Observation a178bc6f-57c9-40e6-a475-212dff4f59f8 · outbound

This paper cites KeyWorld: Key frame rea- soning enables effective and efficient world models,.

Improving Robotic Imitation Learning via Trajectory Standardization KeyWorld: Key frame rea- soning enables effective and efficient world models,

Reference 21

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arxiv_id, observed 2026-07-04T10:39:45.523764Z

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=2026-06-26T08:37:26.864436Z digest=sha256:8d6d76816fba50bc3e2549a55628d14ce9a989494dd8320fc9cfce3daef2583b

Observation e64e637d-9b73-4d0e-8d88-0c1b3d856699 · outbound

This paper cites Algorithms for the reduction of the number of points required to represent a digitized line or its caricature,.

Improving Robotic Imitation Learning via Trajectory Standardization Algorithms for the reduction of the number of points required to represent a digitized line or its caricature,

Reference 22

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Observation 62cce62f-51a6-4e52-b83c-a1e0c16ab07a · outbound

This paper cites Spatiotemporal compression techniques for moving point objects,.

Improving Robotic Imitation Learning via Trajectory Standardization Spatiotemporal compression techniques for moving point objects,

Reference 23

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Observation d9e147d1-fbd1-4648-9cd6-42fad6541560 · outbound

This paper cites Trajectory clustering: a partition- and-group framework,.

Improving Robotic Imitation Learning via Trajectory Standardization Trajectory clustering: a partition- and-group framework,

Reference 24

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Observation 5a2defc2-b0fa-4647-b6a1-cecea27b4feb · outbound

This paper cites an unresolved cited work.

Improving Robotic Imitation Learning via Trajectory Standardization Unresolved cited work

Reference 25

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Pith citing papers

No inbound Pith citation observations are available.