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Paper Citation Record · LEDGER

STRAP: Robot Sub-Trajectory Retrieval for Augmented Policy Learning

As of 21 August 2026, this Paper Citation Record lists 30 of 30 outbound references and 8 inbound Pith citation observations for arXiv:2412.15182.

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

pith.paper-citation-record.v1
2412.15182 v2

Coverage vector

measured 30 of 30 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T11:37:44.840650Z

measured 38 of 38 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 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T21:46:26.664718Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-13T07:52:32.029840Z

Reference resolution

30 of 30 outbound references displayed

  • verified exact0
  • verified fuzzy10
  • unresolved18
  • parse uncertain0
  • malformed identifier2
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 67ee1c6c-8e70-425c-9414-df4395916ece · outbound

This paper cites RT-H: Action Hierarchies Using Language.

STRAP: Robot Sub-Trajectory Retrieval for Augmented Policy Learning RT-H: Action Hierarchies Using Language

Reference 1

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source=pdf_text observed=2026-08-11T11:37:44.739894Z digest=sha256:fde18deafe51c7bb4c285d6c1bdf8e035853f098f14a8a2347500fae8881905d

Observation 9939295c-330a-47f5-89e4-4f744f868ef3 · outbound

This paper cites Our transformer policy was trained for 300 epochs with batch size 32 and an epoch every 200 gradient steps.

STRAP: Robot Sub-Trajectory Retrieval for Augmented Policy Learning Our transformer policy was trained for 300 epochs with batch size 32 and an epoch every 200 gradient steps

Reference 5

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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.

source=pdf_text observed=2026-08-11T11:37:44.822812Z digest=sha256:787ede6105cfbc75cf4493417e3435136653dd730032242459052808cfee20b6

Observation 175fb59c-df76-4f47-aa6c-767d22e4116c · outbound

This paper cites Behavior Retrieval: Few-Shot Imitation Learning by Querying Unlabeled Datasets.

STRAP: Robot Sub-Trajectory Retrieval for Augmented Policy Learning Behavior Retrieval: Few-Shot Imitation Learning by Querying Unlabeled Datasets

Reference 6

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source=pdf_text observed=2026-08-11T11:37:44.758637Z digest=sha256:9fd60b4dbf8b328e18a91c02050de9706669e9c73c2f917c44c73a0ff61e1e47

Observation b55203e2-0138-49d2-b424-0f3c65ab45fb · outbound

This paper cites BAKU: An Efficient Transformer for Multi-Task Policy Learning.

STRAP: Robot Sub-Trajectory Retrieval for Augmented Policy Learning BAKU: An Efficient Transformer for Multi-Task Policy Learning

Reference 7

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source=pdf_text observed=2026-08-11T11:37:44.761932Z digest=sha256:d7014b2ce68272036ec614159adbe6b97a6575d64b2aba18b1e25d87ef1d2f52

Observation 0e87074a-7a87-43d1-be79-595402c072d8 · outbound

This paper cites Deep Residual Learning for Image Recognition.

STRAP: Robot Sub-Trajectory Retrieval for Augmented Policy Learning Deep Residual Learning for Image Recognition

Reference 8

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source=pdf_text observed=2026-08-11T11:37:44.765204Z digest=sha256:c2fd2ebbd42ab483272286035b88af3f3cffebaf49cc6f56d2aa6969063681e9

Observation 7fc905aa-11f2-4a20-9fd0-dd8e8f8cc039 · outbound

This paper cites Zero-shot imitation policy via search in demonstration dataset.

STRAP: Robot Sub-Trajectory Retrieval for Augmented Policy Learning Zero-shot imitation policy via search in demonstration dataset

Reference 11

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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.

source=pdf_text observed=2026-08-11T11:37:44.775412Z digest=sha256:6168156b33d5fe05c1571196f487b9b07ca747e16d5d3fd3d8d3a4bbe8365101

Observation 0f9fa268-b5fb-4f7d-9576-5918de79a1d2 · outbound

This paper cites RoboCasa: Large-Scale Simulation of Everyday Tasks for Generalist Robots.

STRAP: Robot Sub-Trajectory Retrieval for Augmented Policy Learning RoboCasa: Large-Scale Simulation of Everyday Tasks for Generalist Robots

Reference 12

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source=pdf_text observed=2026-08-11T11:37:44.779816Z digest=sha256:4c241983b7e1e8b5656cb51bdfb6b69808e7f33a587343802840a3d72b03281d

Observation 98e1501b-c102-4b5e-a878-7d955b09e454 · outbound

This paper cites Open X-Embodiment: Robotic Learning Datasets and RT-X Models.

STRAP: Robot Sub-Trajectory Retrieval for Augmented Policy Learning Open X-Embodiment: Robotic Learning Datasets and RT-X Models

Reference 13

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source=pdf_text observed=2026-08-11T11:37:44.783166Z digest=sha256:82f97364f0ff1ea19b7472f7be5f257776703ab9d0964abce8086ff7427c4b10

Observation 2367ea09-40de-43c6-9bd5-94d90598d621 · outbound

This paper cites DINOv2: Learning Robust Visual Features without Supervision.

STRAP: Robot Sub-Trajectory Retrieval for Augmented Policy Learning DINOv2: Learning Robust Visual Features without Supervision

Reference 14

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source=pdf_text observed=2026-08-11T11:37:44.787446Z digest=sha256:8e1b9823f2763a2d3740c1c82c8ad2d3b9d52f3f654ac97bc386ed7f17d81c84

Observation 1c0e0db9-59bf-460a-bfc2-e12dda00d997 · outbound

This paper cites R+ x: Retrieval and execution from everyday human videos.

STRAP: Robot Sub-Trajectory Retrieval for Augmented Policy Learning R+ x: Retrieval and execution from everyday human videos

Reference 15

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Source-reported events for the cited work

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

source=pdf_text observed=2026-08-11T11:37:44.791447Z digest=sha256:ca7a469dfb1c2d80e1b064950b85a6eb911e852da2e27656743fa85d9389fa46

Observation 16066a29-0044-4648-87d4-18a67d2c7296 · outbound

This paper cites an unresolved cited work.

STRAP: Robot Sub-Trajectory Retrieval for Augmented Policy Learning Unresolved cited work

Reference 16

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

source=pdf_text observed=2026-08-11T11:37:44.794788Z digest=sha256:1e5c2c8a4cf7a9b6162d41207784a3b59a9e0502eb1bf13d3c0aa7aef8f17f8f

Observation 48519590-1481-4eb3-98f0-bbad04b86425 · outbound

This paper cites 12 Published as a conference paper at ICLR 2025 Tanmay Shankar, Yixin Lin, Aravind Rajeswaran, Vikash Kumar, Stuart Anderson, and Jean Oh.

STRAP: Robot Sub-Trajectory Retrieval for Augmented Policy Learning 12 Published as a conference paper at ICLR 2025 Tanmay Shankar, Yixin Lin, Aravind Rajeswaran, Vikash Kumar, Stuart Anderson, and Jean Oh

Reference 17

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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.

source=pdf_text observed=2026-08-11T11:37:44.798143Z digest=sha256:1e91cbd62c4654ea46df2b865a0813ec4318050c6951722c68d0946c7663fe50

Observation 4ab7cab2-96d6-448c-884a-bb36440a6f57 · outbound

This paper cites Language-conditioned semantic search-based policy for robotic manipulation tasks.

STRAP: Robot Sub-Trajectory Retrieval for Augmented Policy Learning Language-conditioned semantic search-based policy for robotic manipulation tasks

Reference 18

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Source-reported events for the cited work

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

source=pdf_text observed=2026-08-11T11:37:44.801349Z digest=sha256:d657f7437548e05b0a376fdf5ea2cde9399d6b0798d5833395833c6fe5a0baa4

Observation 0896c57e-5122-4400-84a8-c0213ebba73c · outbound

This paper cites Gemini: A Family of Highly Capable Multimodal Models.

STRAP: Robot Sub-Trajectory Retrieval for Augmented Policy Learning Gemini: A Family of Highly Capable Multimodal Models

Reference 19

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source=pdf_text observed=2026-08-11T11:37:44.803484Z digest=sha256:7c7a17cf7131681a9137466e2adec285effd08523d3b25ce5424496518ff896b

Observation 85a7247a-4e9c-429a-9a1d-d3381265799e · outbound

This paper cites PoCo: Policy Composition from and for Heterogeneous Robot Learning.

STRAP: Robot Sub-Trajectory Retrieval for Augmented Policy Learning PoCo: Policy Composition from and for Heterogeneous Robot Learning

Reference 21

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source=pdf_text observed=2026-08-11T11:37:44.809033Z digest=sha256:98571ac7e287120b2be831b6421e1d1fa126aa75c30e70de4c5128a0b56cd24c

Observation de94b426-4465-481b-89b8-65da4964f9c4 · outbound

This paper cites Offline Imitation Learning Through Graph Search and Retrieval.

STRAP: Robot Sub-Trajectory Retrieval for Augmented Policy Learning Offline Imitation Learning Through Graph Search and Retrieval

Reference 22

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source=pdf_text observed=2026-08-11T11:37:44.811777Z digest=sha256:4e4d6a7a00616c933fe23ce98193ef34532b492cb1bfc56aa4f015283e583695

Observation c54bd872-57fd-43af-b7e4-250b30eb49c0 · outbound

This paper cites Distilling and retrieving generalizable knowledge for robot manipulation via language corrections.

STRAP: Robot Sub-Trajectory Retrieval for Augmented Policy Learning Distilling and retrieving generalizable knowledge for robot manipulation via language corrections

Reference 23

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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.

source=pdf_text observed=2026-08-11T11:37:44.815525Z digest=sha256:a5b94ae10d74b492d643a2c24353d9193385b2df8cf77d3f1ce1c769f31d5ec3

Observation 80601bf6-3801-44ac-9199-faae54f57e7d · outbound

This paper cites EXTRACT: Efficient Policy Learning by Extracting Transferable Robot Skills from Offline Data.

STRAP: Robot Sub-Trajectory Retrieval for Augmented Policy Learning EXTRACT: Efficient Policy Learning by Extracting Transferable Robot Skills from Offline Data

Reference 24

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source=pdf_text observed=2026-08-11T11:37:44.819512Z digest=sha256:0fc284eb728c28f8ac1737270f7b13eef5b0f26f73107bc07c82cae6f0f7272f

Observation 3a32eb7c-23a4-4745-afa5-7f0ae1b06b5a · outbound

This paper cites We use the DROID-setup (Khazatsky et al.,.

STRAP: Robot Sub-Trajectory Retrieval for Augmented Policy Learning We use the DROID-setup (Khazatsky et al.,

Reference 26

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Source-reported events for the cited work

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

source=pdf_text observed=2026-08-11T11:37:44.826608Z digest=sha256:72d941b4b1f622e9ea5914884af7d6e020b430ee1abdb4002d8a294c3301b9b6

Observation a84500d7-dde8-43c1-872e-8424e845e000 · outbound

This paper cites A.2.1 F RANKA -PEN-IN-C UP Task: We evaluate STRAP’s ability to retrieve from ”unrelated” tasks in a pen-in-cup scenario.

STRAP: Robot Sub-Trajectory Retrieval for Augmented Policy Learning A.2.1 F RANKA -PEN-IN-C UP Task: We evaluate STRAP’s ability to retrieve from ”unrelated” tasks in a pen-in-cup scenario

Reference 27

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Source-reported events for the cited work

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

source=pdf_text observed=2026-08-11T11:37:44.830649Z digest=sha256:2e64fd7c76d516568e5bba7027919a9e7c30aea756e2b132e120f3d46e8f9537

Observation cf3658f5-0c18-4a21-afa0-e8f1edf1cd84 · outbound

This paper cites These experiments replicate the training setup for BR and FR.

STRAP: Robot Sub-Trajectory Retrieval for Augmented Policy Learning These experiments replicate the training setup for BR and FR

Reference 28

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

source=pdf_text observed=2026-08-11T11:37:44.833966Z digest=sha256:2621d1b5d4218f3fe3e0b19138523a61af403842388f1bd7d227ddca73971d09

Observation 399b54ab-e2f3-4363-b589-217af16dda78 · outbound

This paper cites Note that computing the distance matrix can be expressed as matrix multiplications and can leverage GPU deployment and custom CUDA kernels for even greater speedup.

STRAP: Robot Sub-Trajectory Retrieval for Augmented Policy Learning Note that computing the distance matrix can be expressed as matrix multiplications and can leverage GPU deployment and custom CUDA kernels for even greater speedup

Reference 30

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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.

source=pdf_text observed=2026-08-11T11:37:44.840650Z digest=sha256:a53c5b017e836d927576480b8faa9fa0427f6a3831691055a69e606a3ada8480

Observation c5361802-4dde-4a23-bbb3-e9aecfcb2848 · outbound

This paper cites The wall clock time for encoding the entire DROID dataset ( 18.9M timesteps, single-view) therefore sums up to only 26h.

STRAP: Robot Sub-Trajectory Retrieval for Augmented Policy Learning The wall clock time for encoding the entire DROID dataset ( 18.9M timesteps, single-view) therefore sums up to only 26h

Reference 32

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

source=pdf_text observed=2026-08-11T11:37:44.837264Z digest=sha256:f3346f73eab264c44608af4f25f3a3c03e6c099a89fce828787e087e75984974

Observation ec70440f-a666-458b-bbf4-e2a386cd3769 · outbound

This paper cites DINOBot: Robot Manipulation via Retrieval and Alignment with Vision Foundation Models.

STRAP: Robot Sub-Trajectory Retrieval for Augmented Policy Learning DINOBot: Robot Manipulation via Retrieval and Alignment with Vision Foundation Models

Reference 2009

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source=pdf_text observed=2026-08-11T11:37:44.754479Z digest=sha256:5643654b49c24a29a0ab73a82a0810c0c3e789c9c2d173ee2c4cd723959e7ae4

Observation ff358461-473b-4aad-a507-83fccc2cea23 · outbound

This paper cites Toward General-Purpose Robots via Foundation Models: A Survey and Meta-Analysis.

STRAP: Robot Sub-Trajectory Retrieval for Augmented Policy Learning Toward General-Purpose Robots via Foundation Models: A Survey and Meta-Analysis

Reference 2015

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source=pdf_text observed=2026-08-11T11:37:44.768614Z digest=sha256:e339bb01ece4e7783c3200bef95ea3ab05cc8a3a497da8c998a5abf29988183a

Observation 73858269-0bf5-4617-afae-96d3bbe586ad · outbound

This paper cites PoCo: Policy Composition from and for Heterogeneous Robot Learning.

STRAP: Robot Sub-Trajectory Retrieval for Augmented Policy Learning PoCo: Policy Composition from and for Heterogeneous Robot Learning

Reference 2017

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source=pdf_text observed=2026-08-11T11:37:44.806640Z digest=sha256:75c6542b7f964534d493b0fe3b821f8e45d0e99a2cb93ba2dc0db7aa81142905

Observation 549eda4d-f513-4c9a-a5a8-bc6bb63a9894 · outbound

This paper cites Diffusion Policy: Visuomotor Policy Learning via Action Diffusion.

STRAP: Robot Sub-Trajectory Retrieval for Augmented Policy Learning Diffusion Policy: Visuomotor Policy Learning via Action Diffusion

Reference 2021

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source=pdf_text observed=2026-08-11T11:37:44.747906Z digest=sha256:d8e3e1a061efbc6e3f7b527e9a427bdcb9aa765863d9e3b1262bd7885ac148b6

Observation 1971198e-f97f-495d-98f6-3200ac4c091e · outbound

This paper cites DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset.

STRAP: Robot Sub-Trajectory Retrieval for Augmented Policy Learning DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset

Reference 2022

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source=pdf_text observed=2026-08-11T11:37:44.772343Z digest=sha256:e6ecef4797e5c44c7805abff2e0ccf69522fdeedd3083dbb07dd84c21667f199

Observation 89e2bcf1-474e-4921-bc00-6da89f1a2e80 · outbound

This paper cites Imagenet: A large-scale hi- erarchical image database.

STRAP: Robot Sub-Trajectory Retrieval for Augmented Policy Learning Imagenet: A large-scale hi- erarchical image database

Reference 2023

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source=pdf_text observed=2026-08-11T11:37:44.751424Z digest=sha256:3bb9ac54c23815b72f50c65854e8b8fc75cfc0d054ba5806a72d7f4baafb35f2

Observation bc12c7c2-c5e3-42f2-8b24-fc76a524ba2b · outbound

This paper cites RT-1: Robotics Transformer for Real-World Control at Scale.

STRAP: Robot Sub-Trajectory Retrieval for Augmented Policy Learning RT-1: Robotics Transformer for Real-World Control at Scale

Reference 2024

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source=pdf_text observed=2026-08-11T11:37:44.744609Z digest=sha256:0678b79e095734e2f777c4f3598d2fd81b4a9877950f47a28cf91dd6a0fec87a

Pith citing papers

Observation 9d2bb405-bf19-4ebb-a8b6-a7e11dda1490 · inbound

RT-Cache: Training-Free Retrieval for Real-Time Manipulation cites this paper.

RT-Cache: Training-Free Retrieval for Real-Time Manipulation STRAP: Robot Sub-Trajectory Retrieval for Augmented Policy Learning

Reference 19

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source=pdf_text observed=2026-08-15T21:46:26.664718Z digest=sha256:8e5c6f02bec5fe80df2ca399834867b388315c5d9e99fe0f79ec3dc286399820

Observation bddd0165-b007-4ab0-af48-f6357743a28b · inbound

DataMIL: Selecting Data for Robot Imitation Learning with Datamodels cites this paper.

DataMIL: Selecting Data for Robot Imitation Learning with Datamodels STRAP: Robot Sub-Trajectory Retrieval for Augmented Policy Learning

Reference 19

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source=pdf_text observed=2026-08-15T21:34:17.064231Z digest=sha256:92bba7fc301fcaef8862bebcab0337c4889d8d5f526359b815a906977d9b8125

Observation 9c2bba24-244e-49fa-965d-4ed4c789da99 · inbound

RealDrive: Retrieval-Augmented Driving with Diffusion Models cites this paper.

RealDrive: Retrieval-Augmented Driving with Diffusion Models STRAP: Robot Sub-Trajectory Retrieval for Augmented Policy Learning

Reference 31

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source=pdf_text observed=2026-08-07T12:18:52.809886Z digest=sha256:bc495177a10d9069b9f97881d3e942f1b98240e41173a5f4a17e6cc84a739b9f

Observation e4b5f063-c916-4a45-ad3c-82db84662d32 · inbound

ImMimic: Cross-Domain Imitation from Human Videos via Mapping and Interpolation cites this paper.

ImMimic: Cross-Domain Imitation from Human Videos via Mapping and Interpolation STRAP: Robot Sub-Trajectory Retrieval for Augmented Policy Learning

Reference 14

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source=pdf_text observed=2026-08-04T17:26:19.525579Z digest=sha256:1edd727b67e3059b5e9c8bee3a564c6825a45d16c4da5abcc619f98d90dbb4bb

Observation 6b1bf60c-12a4-4f44-ae8e-3dc85f7dd72e · inbound

Learning from the Best: Smoothness-Driven Metrics for Data Quality in Imitation Learning cites this paper.

Learning from the Best: Smoothness-Driven Metrics for Data Quality in Imitation Learning STRAP: Robot Sub-Trajectory Retrieval for Augmented Policy Learning

Reference 16

Resolution
verified exact
arxiv_id, observed 2026-05-11T19:36:13.275986Z

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.

source=pdf_text observed=2026-05-08T11:36:01.908204Z digest=sha256:d3bba199f2a6ea6cc6a9746716d5131d78ffef0817f82ad76dd260f2d9b0a84d

Observation b1021d86-186b-4b8b-8db8-b519ebafb1e4 · inbound

Retrieve-then-Steer: Online Success Memory for Test-Time Adaptation of Generative VLAs cites this paper.

Retrieve-then-Steer: Online Success Memory for Test-Time Adaptation of Generative VLAs STRAP: Robot Sub-Trajectory Retrieval for Augmented Policy Learning

Reference 17

Resolution
verified exact
arxiv_id, observed 2026-05-12T07:06:27.985035Z

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.

source=pdf_text observed=2026-05-12T03:46:01.737920Z digest=sha256:1a85c4128326974564d200de2f4e6d9867977d7e5db28abbeb2043b209c761c9

Observation 5fbb954f-27ad-49fe-a2e6-361cc39a8646 · inbound

Retrieve-then-Steer: Online Success Memory for Test-Time Adaptation of Generative VLAs cites this paper.

Retrieve-then-Steer: Online Success Memory for Test-Time Adaptation of Generative VLAs STRAP: Robot Sub-Trajectory Retrieval for Augmented Policy Learning

Reference 17

Resolution
verified exact
arxiv_id, observed 2026-05-13T07:52:32.033012Z

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.

source=pdf_text observed=2026-05-13T07:50:13.845621Z digest=sha256:58a1dec6452360cdb2170cc7b63cf1440c266414dfeb5945b5c15585002dd5fe

Observation f48055be-372f-445d-b4c2-2722dead7344 · inbound

Try Once, Then Optimal: De-Redundified Procedure Memory for Cross-Episode Exploration Amortization cites this paper.

Try Once, Then Optimal: De-Redundified Procedure Memory for Cross-Episode Exploration Amortization STRAP: Robot Sub-Trajectory Retrieval for Augmented Policy Learning

Reference 12

Resolution
unresolved
no resolver link, observed 2026-07-30T15:27:16.620705Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-30T15:27:16.620705Z digest=sha256:3769a75ab4d79abf8acccf33feb8f0656dcaec6e22ae414cdde73fa66069411b