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Paper Citation Record · LEDGER

Zero-Shot Sim-to-Real Reinforcement Learning for Fruit Harvesting

As of 21 August 2026, this Paper Citation Record lists 17 of 17 outbound references and 2 inbound Pith citation observations for arXiv:2505.08458.

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

pith.paper-citation-record.v1
2505.08458 v1

Coverage vector

measured 17 of 17 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T21:57:44.440297Z

measured 19 of 19 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-02T03:42:46.322098Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

17 of 17 outbound references displayed

  • verified exact0
  • verified fuzzy11
  • unresolved6
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 6be746e1-568d-44e5-899b-784131c19e56 · outbound

This paper cites The robots that could help kent’s fruit-picking problems,.

Zero-Shot Sim-to-Real Reinforcement Learning for Fruit Harvesting The robots that could help kent’s fruit-picking problems,

Reference 1

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T21:57:44.721980Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 68c05019-d3fb-4494-84ae-eb66f24552d3 · outbound

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

Zero-Shot Sim-to-Real Reinforcement Learning for Fruit Harvesting Serl: A software suite for sample- efficient robotic reinforcement learning,

Reference 2

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T21:57:44.706266Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-15T21:57:44.367831Z digest=sha256:119748f8dae398aa64fa7a7f25aff6559c7587655857640c248f523cbce6fcc9

Observation 9d91a8f3-2dce-4bc8-ad99-7f8c97766881 · outbound

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

Zero-Shot Sim-to-Real Reinforcement Learning for Fruit Harvesting Precise and Dexterous Robotic Manipulation via Human-in-the-Loop Reinforcement Learning

Reference 3

Resolution
unresolved
no resolver link, observed 2026-08-15T21:57:44.372602Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T21:57:44.372602Z digest=sha256:1fefa7cd1478d94d39b40e405d14186fbf5cb9f677abc049946c4a8a14936e9d

Observation 920aa4fe-e683-42c5-85da-40eb85d8aedc · outbound

This paper cites Mujoco: A physics engine for model-based control,.

Zero-Shot Sim-to-Real Reinforcement Learning for Fruit Harvesting Mujoco: A physics engine for model-based control,

Reference 4

Resolution
unresolved
no resolver link, observed 2026-08-15T21:57:44.377633Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T21:57:44.377633Z digest=sha256:cd1086860ad50eeae5a79b4ecbd6c89060dfc8e5b030648830a722863a8c37f7

Observation d333d1d8-1610-4dd9-a524-d5712c4263bb · outbound

This paper cites Gymnasium: A Standard Interface for Reinforcement Learning Environments.

Zero-Shot Sim-to-Real Reinforcement Learning for Fruit Harvesting Gymnasium: A Standard Interface for Reinforcement Learning Environments

Reference 5

Resolution
unresolved
no resolver link, observed 2026-08-15T21:57:44.382677Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T21:57:44.382677Z digest=sha256:7a7d402b346dfb8c9e18fef7c5e9e802153cdab5ad57ed51d9341150749d5157

Observation 6a605cbc-1a83-46d5-b390-d8f2587e6a8e · outbound

This paper cites Sim-to-real transfer in deep reinforcement learning for robotics: a survey,.

Zero-Shot Sim-to-Real Reinforcement Learning for Fruit Harvesting Sim-to-real transfer in deep reinforcement learning for robotics: a survey,

Reference 6

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T21:57:44.681091Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-15T21:57:44.388081Z digest=sha256:9f176d183f39b9014494b62d25659e88dcaa2ce17c9daea1c61335b496ed2d71

Observation 176bad5c-62b2-4482-b300-e3c2588fc5a2 · outbound

This paper cites Understanding Domain Randomization for Sim-to-real Transfer.

Zero-Shot Sim-to-Real Reinforcement Learning for Fruit Harvesting Understanding Domain Randomization for Sim-to-real Transfer

Reference 7

Resolution
unresolved
no resolver link, observed 2026-08-15T21:57:44.393380Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T21:57:44.393380Z digest=sha256:c8d2b9c3249df5e46c73ac501ff18317067a294d2ee435ddc1b422ff50900e4c

Observation 469e151d-e49f-442e-a216-3e43cdf8d3ae · outbound

This paper cites Domain randomization for transferring deep neural networks from simulation to the real world,.

Zero-Shot Sim-to-Real Reinforcement Learning for Fruit Harvesting Domain randomization for transferring deep neural networks from simulation to the real world,

Reference 8

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T21:57:44.666044Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-15T21:57:44.398196Z digest=sha256:950298beadefc6b4fa3bcf386f29409ad0692a6990ad022c22e45e66219798ee

Observation 8073a9a7-d823-46ff-85ad-996dfc9a2ff6 · outbound

This paper cites Reaching pruning locations in a vine using a deep reinforcement learning policy,.

Zero-Shot Sim-to-Real Reinforcement Learning for Fruit Harvesting Reaching pruning locations in a vine using a deep reinforcement learning policy,

Reference 9

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T21:57:44.651187Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-15T21:57:44.402829Z digest=sha256:367d8eacd5f36805fb6fce990870720f848e3bf9a85bc2336e61d71e20540c87

Observation a269a306-75f9-482e-893e-2b5ae9e19be1 · outbound

This paper cites An inverse kinematics solution for a series-parallel hybrid banana-harvesting robot based on deep reinforcement learning,.

Zero-Shot Sim-to-Real Reinforcement Learning for Fruit Harvesting An inverse kinematics solution for a series-parallel hybrid banana-harvesting robot based on deep reinforcement learning,

Reference 10

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T21:57:44.635214Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-15T21:57:44.407427Z digest=sha256:3a26f8d3eebf5220943514cf590743e41d12ceee430b22e3049977d758774b68

Observation ab5880e1-6ef8-49f8-b149-54f5336b82dd · outbound

This paper cites Deep reinforcement learning for robotic pushing and picking in cluttered environment,.

Zero-Shot Sim-to-Real Reinforcement Learning for Fruit Harvesting Deep reinforcement learning for robotic pushing and picking in cluttered environment,

Reference 11

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T21:57:44.618165Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-15T21:57:44.412214Z digest=sha256:14d024ecda7f697ce3f12a77c6e6c79ab5da884bd27b707e985e9ee910d3edae

Observation dc65e900-977c-4950-a084-39dda4c74205 · outbound

This paper cites Sim-to-real reinforcement learning for deformable object manipulation,.

Zero-Shot Sim-to-Real Reinforcement Learning for Fruit Harvesting Sim-to-real reinforcement learning for deformable object manipulation,

Reference 12

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T21:57:44.602518Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-15T21:57:44.416643Z digest=sha256:4f2c4eafbabb3a55653aae4f9fccb92a266538063fac74fd7ae718bd441f18fe

Observation 0411213f-72a7-4f2b-9f13-c0056b9b38d7 · outbound

This paper cites Recognition and localization methods for vision-based fruit picking robots: A review,.

Zero-Shot Sim-to-Real Reinforcement Learning for Fruit Harvesting Recognition and localization methods for vision-based fruit picking robots: A review,

Reference 13

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T21:57:44.586996Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-15T21:57:44.421159Z digest=sha256:cb9717562f1a27baecb65a8892b88996eaaa6e714ff2cabf5e118192aadd66d5

Observation 02e53b42-5397-44ba-b321-bc3ff827c7f3 · outbound

This paper cites DrM: Mastering Visual Reinforcement Learning through Dormant Ratio Minimization.

Zero-Shot Sim-to-Real Reinforcement Learning for Fruit Harvesting DrM: Mastering Visual Reinforcement Learning through Dormant Ratio Minimization

Reference 14

Resolution
unresolved
no resolver link, observed 2026-08-15T21:57:44.425897Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T21:57:44.425897Z digest=sha256:101e2616b3737ef85d96bca33b59faf2bf910349d79988c3c8310ca7210471ef

Observation 4fd5122f-bb65-4445-9a46-de4f61259236 · outbound

This paper cites Mastering Visual Continuous Control: Improved Data-Augmented Reinforcement Learning.

Zero-Shot Sim-to-Real Reinforcement Learning for Fruit Harvesting Mastering Visual Continuous Control: Improved Data-Augmented Reinforcement Learning

Reference 15

Resolution
unresolved
no resolver link, observed 2026-08-15T21:57:44.431174Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T21:57:44.431174Z digest=sha256:1f5148c9e2f8677a62937f39dbfeae826a9778a21fe9c8ceb85e7a6dae0578b1

Observation 2f06946f-3920-412c-8abf-9d5fd41ceea8 · outbound

This paper cites Deep recurrent q-learning for partially observable mdps.,.

Zero-Shot Sim-to-Real Reinforcement Learning for Fruit Harvesting Deep recurrent q-learning for partially observable mdps.,

Reference 16

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T21:57:44.571088Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-15T21:57:44.435906Z digest=sha256:58550ce51f573fa195d49e7c888fb49748e52c0b072422735e7276a4ca61f708

Observation 4cffd7f3-de5e-483a-bf4f-14f5f1a319b9 · outbound

This paper cites Mujoco playground,.

Zero-Shot Sim-to-Real Reinforcement Learning for Fruit Harvesting Mujoco playground,

Reference 17

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T21:57:44.554330Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-15T21:57:44.440297Z digest=sha256:a986ef9e1aa15366d3613a9c5082c62299c674d495e010b4bc585a8a6cc1fa33

Pith citing papers

Observation 23272e98-c810-44c8-bb8b-252f37d7a97c · inbound

Vision-Based Obstacle Separation for Strawberry Harvesting in Clusters Using Hierarchical Reinforcement Learning cites this paper.

Vision-Based Obstacle Separation for Strawberry Harvesting in Clusters Using Hierarchical Reinforcement Learning Zero-Shot Sim-to-Real Reinforcement Learning for Fruit Harvesting

Reference 6

Resolution
unresolved
no resolver link, observed 2026-08-02T03:42:46.322098Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T03:42:46.322098Z digest=sha256:4ec7bcdb39a420f3d6f60a43e2c32a700f6911f34b5327281426cf3a3f003c4c

Observation 9a4dcf4e-dd93-4f9e-9089-34d0dfa51cd6 · inbound

Reinforcement Learning for the Full Strawberry Harvesting Process: Obstacle Separation, Detachment, and Placement cites this paper.

Reinforcement Learning for the Full Strawberry Harvesting Process: Obstacle Separation, Detachment, and Placement Zero-Shot Sim-to-Real Reinforcement Learning for Fruit Harvesting

Reference 6

Resolution
unresolved
no resolver link, observed 2026-08-02T01:23:37.596554Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T01:23:37.596554Z digest=sha256:a923e20e3a67c0e44e16abdf573cd9fc37c6537fdca5481713b2f5f4d6d788eb