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Paper Citation Record · LEDGER

Construction of a Multiple-DOF Under-actuated Gripper with Force-Sensing via Deep Learning

As of 19 August 2026, this Paper Citation Record lists 25 of 25 outbound references and 0 inbound Pith citation observations for arXiv:2506.11570.

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

pith.paper-citation-record.v1
2506.11570 v1

Coverage vector

measured 25 of 25 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T04:09:17.069764Z

measured 25 of 25 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+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 exact0
  • verified fuzzy24
  • unresolved1
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 2f4b7510-b84c-4530-9a68-ffaf9635a51b · outbound

This paper cites The maximum grasping force can be in general Figure.

Construction of a Multiple-DOF Under-actuated Gripper with Force-Sensing via Deep Learning The maximum grasping force can be in general Figure

Reference 1

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T04:09:22.244135Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T04:09:14.820603Z digest=sha256:4237872504d82f4f8740a481e04de29009959cbe87bbb85a0a03f92686e59c82

Observation 973c60a0-3a5e-4603-9064-d8797cac67d7 · outbound

This paper cites The relationship between the force applied to the elastic and the deformation of the elastic ring needs to be built.

Construction of a Multiple-DOF Under-actuated Gripper with Force-Sensing via Deep Learning The relationship between the force applied to the elastic and the deformation of the elastic ring needs to be built

Reference 2

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T04:09:22.098195Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T04:09:14.914729Z digest=sha256:fba22ba008dfb1ff04edf3bf5707a2a5c55df3e4d2ea2234171bde3161ece26c

Observation dbcd4998-4f62-4393-8b6c-6933aa760625 · outbound

This paper cites GL-Robot can grasp a coin with a thickness of 1.85mm in the parallel grasping mode and a cubic object with a length of 125mm in the enveloping grasping mode.

Construction of a Multiple-DOF Under-actuated Gripper with Force-Sensing via Deep Learning GL-Robot can grasp a coin with a thickness of 1.85mm in the parallel grasping mode and a cubic object with a length of 125mm in the enveloping grasping mode

Reference 3

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T04:09:21.920861Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T04:09:14.981753Z digest=sha256:fff7bbefef2339162b0d2cfdbb7b69c5766f8c5f6a1dc5318f516573a76996f1

Observation 35690ca4-efca-4808-a17a-ac60fd2779b7 · outbound

This paper cites Real- time robotic manipulation of cylindrical objects in dynamic scenarios through elliptic shape primitives,.

Construction of a Multiple-DOF Under-actuated Gripper with Force-Sensing via Deep Learning Real- time robotic manipulation of cylindrical objects in dynamic scenarios through elliptic shape primitives,

Reference 4

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T04:09:21.760210Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T04:09:15.085763Z digest=sha256:9248f7f5a0ebf57f49a2f18ee4aba5f75065a65205834a9a0686c03bf07fa8ab

Observation 7c2f09b1-174a-44a1-a465-3eefdf7418b0 · outbound

This paper cites Object Pose Estimation via Pruned Hough Forest With Combined Split Schemes for Robotic Grasp,.

Construction of a Multiple-DOF Under-actuated Gripper with Force-Sensing via Deep Learning Object Pose Estimation via Pruned Hough Forest With Combined Split Schemes for Robotic Grasp,

Reference 5

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T04:09:21.551853Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T04:09:15.184145Z digest=sha256:84d5610067fb565aba9446cf5cf2c35fdf9ac43c268056b1efdeb8264a627a6f

Observation 66c4e018-8c87-43ce-8f90-0b9fa33bac10 · outbound

This paper cites Geometric design optimization of an under- actuated tendon-driven robotic gripper,.

Construction of a Multiple-DOF Under-actuated Gripper with Force-Sensing via Deep Learning Geometric design optimization of an under- actuated tendon-driven robotic gripper,

Reference 6

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T04:09:21.357889Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T04:09:15.282478Z digest=sha256:4894b4c8741b1c5572552ba91e6b757977ca2abae376c990b4ed706b68c83db8

Observation 8c40711c-8d86-416a-b9d1-2a7c28414ed5 · outbound

This paper cites The Velo gripper: A versatile single-actuator design for enveloping, paralle l and fingertip grasps,.

Construction of a Multiple-DOF Under-actuated Gripper with Force-Sensing via Deep Learning The Velo gripper: A versatile single-actuator design for enveloping, paralle l and fingertip grasps,

Reference 7

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T04:09:21.192363Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T04:09:15.382611Z digest=sha256:eda930c493db9b7af54ff81cc5c74c1ed4fe9ce2dfc431a1d683332251531d30

Observation 400ae0fc-3bf2-439d-b36f-eee055435d18 · outbound

This paper cites Underactuated adaptive gripper using flexural buckling,.

Construction of a Multiple-DOF Under-actuated Gripper with Force-Sensing via Deep Learning Underactuated adaptive gripper using flexural buckling,

Reference 8

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T04:09:21.012999Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T04:09:15.477880Z digest=sha256:d0a1112c469733f7c0f4573e5211d23410441475685c4c0155922cf678612e96

Observation 056aaf32-42f6-46e1-9b66-39a937631659 · outbound

This paper cites Design and Implementation of a Multi-Function Gripper for Grasping General Objects,.

Construction of a Multiple-DOF Under-actuated Gripper with Force-Sensing via Deep Learning Design and Implementation of a Multi-Function Gripper for Grasping General Objects,

Reference 9

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T04:09:20.836086Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T04:09:15.551951Z digest=sha256:8ad411c2a33da62adf2ec41cdf30d24af31b6b194a043c6a22f23808fd6c6ee1

Observation 1f48100e-1c3a-4d6b-81fa-484991fc3c2c · outbound

This paper cites Analysis of fingertip force vector for pinch-lifting gripper with robust adaptation to environments,.

Construction of a Multiple-DOF Under-actuated Gripper with Force-Sensing via Deep Learning Analysis of fingertip force vector for pinch-lifting gripper with robust adaptation to environments,

Reference 10

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T04:09:20.676285Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T04:09:15.673552Z digest=sha256:d1f8961c3635101129ecb93263c7a0ec968749be310aa6d283f4447fdba9fba9

Observation e0e609ed-5487-4648-8580-1f0c7b21c23c · outbound

This paper cites Actuation system for highly underactuated gripping mechanism,.

Construction of a Multiple-DOF Under-actuated Gripper with Force-Sensing via Deep Learning Actuation system for highly underactuated gripping mechanism,

Reference 11

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T04:09:20.463305Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T04:09:15.789887Z digest=sha256:4386e3edad19f4a72d2746d8fe6f4cb402c63c45e55469dc3a197dfa17656de4

Observation c642b088-837d-42dc-9be5-e72b3c465489 · outbound

This paper cites Underactuated mechanical finger with return actuation,.

Construction of a Multiple-DOF Under-actuated Gripper with Force-Sensing via Deep Learning Underactuated mechanical finger with return actuation,

Reference 12

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T04:09:20.233805Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T04:09:15.882378Z digest=sha256:45d05f145e5e11173af1a6580dd21f0af2d7a78e5276e5788437f5f1db7360b6

Observation bc92c6af-f665-4c4a-8648-9f1a59d2c4ce · outbound

This paper cites Discrete Cosserat approach for soft robot dynamics: A new piece-wise constant strain model with torsion and shears,.

Construction of a Multiple-DOF Under-actuated Gripper with Force-Sensing via Deep Learning Discrete Cosserat approach for soft robot dynamics: A new piece-wise constant strain model with torsion and shears,

Reference 13

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T04:09:20.023666Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T04:09:15.922325Z digest=sha256:447c6652c037d1f0d60cd58928736bc6b7a870c6b6fcda1888f9baa032e97971

Observation 9a0f20ca-4b2c-414b-b3d8-6a35dde5c726 · outbound

This paper cites A Compliant Adaptive Gripper and Its Intrinsic Force Sensing Method,.

Construction of a Multiple-DOF Under-actuated Gripper with Force-Sensing via Deep Learning A Compliant Adaptive Gripper and Its Intrinsic Force Sensing Method,

Reference 14

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T04:09:19.861565Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T04:09:15.990171Z digest=sha256:74a2375deb6fd10e0e0a1edecb15e440aa32387fba40e0d774249c3ea8c7fccc

Observation 3646d1f2-adde-49ca-8184-48db8649a863 · outbound

This paper cites Gelsight fin ray: Incorporating tactile sensing into a soft co mpliant robotic gripper,.

Construction of a Multiple-DOF Under-actuated Gripper with Force-Sensing via Deep Learning Gelsight fin ray: Incorporating tactile sensing into a soft co mpliant robotic gripper,

Reference 15

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T04:09:19.581804Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T04:09:16.068507Z digest=sha256:f43b6e06b29eb2e57f78dfe68c7ab0809b0860dddb0869b7d706db1f7ebb4f7a

Observation 3c68988e-7793-470c-a30c-761f4a9e43d9 · outbound

This paper cites A compliant adaptive gripper and its intrinsic force sensing method,.

Construction of a Multiple-DOF Under-actuated Gripper with Force-Sensing via Deep Learning A compliant adaptive gripper and its intrinsic force sensing method,

Reference 16

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T04:09:19.320535Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T04:09:16.170240Z digest=sha256:3fe64d847bf87c043251610cb9dfb725f1b8c6fc489bc5e175c7375a3166bafc

Observation f56ac990-db9c-4f0d-9694-594aa2dc523b · outbound

This paper cites Construction of interaction parallel manipulator: towards rehabilitation massage,.

Construction of a Multiple-DOF Under-actuated Gripper with Force-Sensing via Deep Learning Construction of interaction parallel manipulator: towards rehabilitation massage,

Reference 17

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T04:09:19.123146Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T04:09:16.267152Z digest=sha256:ec4e4e2f0b0054b15c9ea209d3b96ddfc2f3eb79b2fbe69f8599354545f93fd8

Observation dbf82b35-eead-433f-8195-11426505df27 · outbound

This paper cites Gsg: A granary-shaped soft gripper with mechanical sensing via snap- through structure,.

Construction of a Multiple-DOF Under-actuated Gripper with Force-Sensing via Deep Learning Gsg: A granary-shaped soft gripper with mechanical sensing via snap- through structure,

Reference 18

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T04:09:18.916898Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T04:09:16.358346Z digest=sha256:84898af9c65d3057db3a1555946680ee8fc1131ecc07c204d0afff428079adb4

Observation 72ccb4ec-9b07-485a-9ef9-d1dfd432f8c2 · outbound

This paper cites Force control of a 3D printed soft gripper with bu ilt-in pneumatic touch sensing chambers,.

Construction of a Multiple-DOF Under-actuated Gripper with Force-Sensing via Deep Learning Force control of a 3D printed soft gripper with bu ilt-in pneumatic touch sensing chambers,

Reference 19

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T04:09:18.722410Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T04:09:16.436861Z digest=sha256:f0db28b338131a5bc972d498556a5b0dce39e0db91910cf2c42313d85d105794

Observation ec298b4a-3ef1-49b3-893f-f05988144984 · outbound

This paper cites Push-on push-off: A compliant bistable gr ipper with mechanical sensing and actuation,.

Construction of a Multiple-DOF Under-actuated Gripper with Force-Sensing via Deep Learning Push-on push-off: A compliant bistable gr ipper with mechanical sensing and actuation,

Reference 20

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T04:09:18.503154Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T04:09:16.540962Z digest=sha256:0104e2143d9ef763934fcb5e34005f0f043f96ff238472c44b3119c614eb68d7

Observation 59c60fdb-f0ac-4958-b0a4-f7dc6295e72b · outbound

This paper cites A bistable soft gri pper with mechanically embedded sensing and actuation for fast grasping,.

Construction of a Multiple-DOF Under-actuated Gripper with Force-Sensing via Deep Learning A bistable soft gri pper with mechanically embedded sensing and actuation for fast grasping,

Reference 21

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T04:09:18.344817Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T04:09:16.627908Z digest=sha256:3bd9a243d1590bd59ba909f7ad5d0b82e93d415d304d199696854e15c0bdba1e

Observation 5c81cfd5-7ff9-43c7-9a39-8279fdff12d6 · outbound

This paper cites H ybrid motion/force control of multi- backbone continuum robots,.

Construction of a Multiple-DOF Under-actuated Gripper with Force-Sensing via Deep Learning H ybrid motion/force control of multi- backbone continuum robots,

Reference 22

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T04:09:18.091989Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T04:09:16.732427Z digest=sha256:bb0b6c1a1263f8968433eae6486354f37b8348d34c051eeb884ff7b927ce89ac

Observation 8ece1ba9-02bc-4812-84a3-0aff3a4c34e9 · outbound

This paper cites Shape-reconstruction-based force sensing method for continuum surgical robots with large deformation,.

Construction of a Multiple-DOF Under-actuated Gripper with Force-Sensing via Deep Learning Shape-reconstruction-based force sensing method for continuum surgical robots with large deformation,

Reference 23

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T04:09:17.902327Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T04:09:16.821525Z digest=sha256:290270b26aae904657ca5ce6b2b82f585388b97e79308419169008b52530824a

Observation ccedf1c7-a58b-4956-bd76-6a112ca1ba5a · outbound

This paper cites an unresolved cited work.

Construction of a Multiple-DOF Under-actuated Gripper with Force-Sensing via Deep Learning Unresolved cited work

Reference 24

Resolution
unresolved
raw_fallback, observed 2026-08-07T04:09:17.718495Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T04:09:16.964084Z digest=sha256:532d3f1c663ff6c6a6162ceb655d37ddeb9f87cdd93f8beebb095a1f71aadeb7

Observation 84d28777-90d1-4cff-bcfa-31d47ae65806 · outbound

This paper cites Kinetostatic analysis of underactuated fingers,.

Construction of a Multiple-DOF Under-actuated Gripper with Force-Sensing via Deep Learning Kinetostatic analysis of underactuated fingers,

Reference 25

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T04:09:17.336802Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T04:09:17.069764Z digest=sha256:c48a8b802b85c30144d130054c75d75808d3c607624dadf8ae23fef2bc22c2d5

Pith citing papers

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