Paper Citation Record · LEDGER

Physics-informed Neural Time Fields for Prehensile Object Manipulation

As of 22 August 2026, this Paper Citation Record lists 38 of 38 outbound references and 0 inbound Pith citation observations for arXiv:2508.02976.

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

pith.paper-citation-record.v1
2508.02976 v1

Coverage vector

measured 38 of 38 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T04:51:48.914124Z

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 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

38 of 38 outbound references displayed

verified exact2
verified fuzzy27
unresolved9
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 2e3bc213-9cf8-4e1f-9ca9-0f528f218ccf · outbound

This paper cites Trends and challenges in robot manipulation.

Physics-informed Neural Time Fields for Prehensile Object Manipulation Trends and challenges in robot manipulation

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T04:51:53.450667Z

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.

Observation 2bca1ae4-d110-4ed1-8c08-4d8bdfcf56d4 · outbound

This paper cites Physics- informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations.

Physics-informed Neural Time Fields for Prehensile Object Manipulation Physics- informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-06T04:51:45.746886Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e28dd9ed-65d9-4bf9-a218-1f62286baddf · outbound

This paper cites NTFields: Neural Time Fields for Physics-Informed Robot Motion Planning.

Physics-informed Neural Time Fields for Prehensile Object Manipulation NTFields: Neural Time Fields for Physics-Informed Robot Motion Planning

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-06T04:51:45.845485Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b7a6a0b7-7fb0-43ef-81ee-ed92e831d280 · outbound

This paper cites Progressive Learning for Physics-informed Neural Motion Planning.

Physics-informed Neural Time Fields for Prehensile Object Manipulation Progressive Learning for Physics-informed Neural Motion Planning

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-06T04:51:45.935071Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0eee512d-cc46-4b88-b41c-6f3814a01972 · outbound

This paper cites Constrained sampling- based planning for grasping and manipulation.

Physics-informed Neural Time Fields for Prehensile Object Manipulation Constrained sampling- based planning for grasping and manipulation

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T04:51:53.314747Z

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.

Observation 897a7cdf-a96d-4d0f-af4e-65aa051c0427 · outbound

This paper cites Robot kinematics: Forward and inverse kinematics.

Physics-informed Neural Time Fields for Prehensile Object Manipulation Robot kinematics: Forward and inverse kinematics

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T04:51:53.172142Z

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.

Observation bdc209e5-aa66-432b-bc68-259840f55780 · outbound

This paper cites Search- based planning for manipulation with motion primitives.

Physics-informed Neural Time Fields for Prehensile Object Manipulation Search- based planning for manipulation with motion primitives

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T04:51:52.978512Z

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.

Observation 7ede6df5-8269-4c8f-8dc8-544f0fc423d3 · outbound

This paper cites Manipulation with Shared Grasping.

Physics-informed Neural Time Fields for Prehensile Object Manipulation Manipulation with Shared Grasping

Reference 8

Resolution: verified exact; local_arxiv, observed 2026-08-06T04:51:49.296671Z

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.

Observation ae336471-d033-4180-a9a9-b19a26412635 · outbound

This paper cites Visual detection of opportunities to exploit contact in grasping using contextual multi-armed bandits.

Physics-informed Neural Time Fields for Prehensile Object Manipulation Visual detection of opportunities to exploit contact in grasping using contextual multi-armed bandits

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T04:51:52.797479Z

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.

Observation dbbedbb6-d91d-47ed-84f4-fda0bf972c08 · outbound

This paper cites Pick and place without geometric object models.

Physics-informed Neural Time Fields for Prehensile Object Manipulation Pick and place without geometric object models

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T04:51:52.672806Z

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.

Observation 470abf52-7e93-4c20-ba41-35d09b1c7482 · outbound

This paper cites Grasping in the wild: Learning 6dof closed-loop grasping from low-cost demonstrations.

Physics-informed Neural Time Fields for Prehensile Object Manipulation Grasping in the wild: Learning 6dof closed-loop grasping from low-cost demonstrations

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T04:51:52.520853Z

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.

Observation a772d0c1-f319-44a7-a01a-f8b33719f322 · outbound

This paper cites A framework for behavioural cloning.

Physics-informed Neural Time Fields for Prehensile Object Manipulation A framework for behavioural cloning

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T04:51:52.356283Z

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.

Observation d4bf2db3-ef74-4b8a-aee5-1f24ea5f0c1c · outbound

This paper cites Algorithms for inverse reinforce- ment learning.

Physics-informed Neural Time Fields for Prehensile Object Manipulation Algorithms for inverse reinforce- ment learning

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T04:51:52.230960Z

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.

Observation e2cedb1b-730d-45e8-9f68-bfadffab58e6 · outbound

This paper cites Learn- ing manipulation actions from human demonstrations.

Physics-informed Neural Time Fields for Prehensile Object Manipulation Learn- ing manipulation actions from human demonstrations

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T04:51:52.083168Z

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.

Observation 81a1fcd5-f100-4584-a9f5-02aadeba8396 · outbound

This paper cites Neural descriptor fields: Se (3)-equivariant object representations for manipulation.

Physics-informed Neural Time Fields for Prehensile Object Manipulation Neural descriptor fields: Se (3)-equivariant object representations for manipulation

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T04:51:51.934191Z

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.

Observation f0622d77-f819-409a-92f1-c9913aac0b7f · outbound

This paper cites Useek: Unsupervised se (3)-equivariant 3d keypoints for generalizable manipulation.

Physics-informed Neural Time Fields for Prehensile Object Manipulation Useek: Unsupervised se (3)-equivariant 3d keypoints for generalizable manipulation

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T04:51:51.750868Z

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.

Observation b757b7d0-269e-45a7-b8b1-8e6cec508886 · outbound

This paper cites QT-Opt: Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulation.

Physics-informed Neural Time Fields for Prehensile Object Manipulation QT-Opt: Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulation

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-06T04:51:47.282789Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 811518d4-2eb0-47e2-bc7b-7f8cf408ae17 · outbound

This paper cites Hacman: Learning hybrid actor-critic maps for 6d non- prehensile manipulation.

Physics-informed Neural Time Fields for Prehensile Object Manipulation Hacman: Learning hybrid actor-critic maps for 6d non- prehensile manipulation

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T04:51:51.622827Z

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.

Observation 31d38e89-7f6e-49c1-9f26-990b3a01c845 · outbound

This paper cites Synergistic task and motion planning with reinforcement learning-based non-prehensile actions.

Physics-informed Neural Time Fields for Prehensile Object Manipulation Synergistic task and motion planning with reinforcement learning-based non-prehensile actions

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T04:51:51.465240Z

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.

Observation 7fb8b7d6-3e63-4c1a-b178-4347935ddbdd · outbound

This paper cites Rearrangement with nonprehensile manipulation using deep reinforcement learning.

Physics-informed Neural Time Fields for Prehensile Object Manipulation Rearrangement with nonprehensile manipulation using deep reinforcement learning

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T04:51:51.285557Z

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.

Observation 8eb30b6e-e199-466c-885a-61ced9b4ad43 · outbound

This paper cites Multi-Stage Reinforcement Learning for Non-Prehensile Manipulation.

Physics-informed Neural Time Fields for Prehensile Object Manipulation Multi-Stage Reinforcement Learning for Non-Prehensile Manipulation

Reference 21

Resolution: verified exact; local_arxiv, observed 2026-08-06T04:51:49.136225Z

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.

Observation 268c4950-724f-47c4-b1bb-73f81d960264 · outbound

This paper cites Beyond pick-and-place: Tackling robotic stacking of diverse shapes.

Physics-informed Neural Time Fields for Prehensile Object Manipulation Beyond pick-and-place: Tackling robotic stacking of diverse shapes

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T04:51:51.142818Z

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.

Observation 408e19c3-a324-4b03-ba4e-1a1dc72a827a · outbound

This paper cites Deep Reinforcement Learning for Robotic Manipulation with Asynchronous Off-Policy Updates.

Physics-informed Neural Time Fields for Prehensile Object Manipulation Deep Reinforcement Learning for Robotic Manipulation with Asynchronous Off-Policy Updates

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-06T04:51:47.744798Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2ee9f6c1-e106-485c-b6d6-5fc0fa320961 · outbound

This paper cites Self-organizing neural networks integrating domain knowledge and reinforcement learning.

Physics-informed Neural Time Fields for Prehensile Object Manipulation Self-organizing neural networks integrating domain knowledge and reinforcement learning

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T04:51:51.008117Z

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.

Observation 7d7a7acb-5348-488c-b94c-8d22f1a01526 · outbound

This paper cites A fast marching level set method for monotonically advancing fronts.

Physics-informed Neural Time Fields for Prehensile Object Manipulation A fast marching level set method for monotonically advancing fronts

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T04:51:50.853413Z

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.

Observation c621a71c-2642-4772-a4f3-5a53bd39aa60 · outbound

This paper cites A fast marching algorithm for the factored eikonal equation.

Physics-informed Neural Time Fields for Prehensile Object Manipulation A fast marching algorithm for the factored eikonal equation

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T04:51:50.718690Z

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.

Observation 5961bb68-f1e3-49b7-a262-58b8d6c3e4a2 · outbound

This paper cites Random features for large-scale kernel machines.

Physics-informed Neural Time Fields for Prehensile Object Manipulation Random features for large-scale kernel machines

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-06T04:51:48.152330Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f28d8d78-e183-4aa3-9b43-c95f9185eacd · outbound

This paper cites Fourier features let networks learn high frequency functions in low dimensional domains.

Physics-informed Neural Time Fields for Prehensile Object Manipulation Fourier features let networks learn high frequency functions in low dimensional domains

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T04:51:50.582249Z

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.

Observation 44a95a8e-193e-4cb2-965e-68decd309407 · outbound

This paper cites Pointnet++: Deep hierarchical feature learning on point sets in a metric space.

Physics-informed Neural Time Fields for Prehensile Object Manipulation Pointnet++: Deep hierarchical feature learning on point sets in a metric space

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-06T04:51:48.262919Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c01d287f-67c8-4d50-afc3-0f54f164e6f6 · outbound

This paper cites Contact-graspnet: Efficient 6-dof grasp generation in cluttered scenes.

Physics-informed Neural Time Fields for Prehensile Object Manipulation Contact-graspnet: Efficient 6-dof grasp generation in cluttered scenes

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T04:51:50.442504Z

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.

Observation 4a41cf04-e11e-46f6-881c-492e68845336 · outbound

This paper cites Riemannian Motion Policies.

Physics-informed Neural Time Fields for Prehensile Object Manipulation Riemannian Motion Policies

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-06T04:51:48.384300Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3653d58f-39d0-48dc-844d-6e2d117f7433 · outbound

This paper cites Benchmarking in Manipulation Research: The YCB Object and Model Set and Benchmarking Protocols.

Physics-informed Neural Time Fields for Prehensile Object Manipulation Benchmarking in Manipulation Research: The YCB Object and Model Set and Benchmarking Protocols

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-06T04:51:48.462952Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5950ba53-4270-44e6-a2c8-b726f1ca126d · outbound

This paper cites Fast marching farthest point sampling.

Physics-informed Neural Time Fields for Prehensile Object Manipulation Fast marching farthest point sampling

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T04:51:50.293630Z

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.

Observation 94f5d85e-8160-445d-bb8d-b60ecacc9c8c · outbound

This paper cites Rrt-connect: An efficient approach to single-query path planning.

Physics-informed Neural Time Fields for Prehensile Object Manipulation Rrt-connect: An efficient approach to single-query path planning

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T04:51:50.153417Z

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.

Observation 6c22f395-1a9f-441b-aa77-06ac1d6f14ed · outbound

This paper cites Fast, anytime motion planning for prehensile manipulation in clutter.

Physics-informed Neural Time Fields for Prehensile Object Manipulation Fast, anytime motion planning for prehensile manipulation in clutter

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T04:51:49.971125Z

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.

Observation 85267711-1bd2-4dc4-85d9-cd38a6b2c3a1 · outbound

This paper cites The open motion planning library.

Physics-informed Neural Time Fields for Prehensile Object Manipulation The open motion planning library

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T04:51:49.793622Z

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.

Observation a6e8027d-2e4b-4300-9a8d-8f95a5725949 · outbound

This paper cites Fcl: A general purpose library for collision and proximity queries.

Physics-informed Neural Time Fields for Prehensile Object Manipulation Fcl: A general purpose library for collision and proximity queries

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T04:51:49.632517Z

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.

Observation 1aeb2dfd-059f-4113-8643-bad9ef2a8291 · outbound

This paper cites Sampling-based algorithms for optimal motion planning.

Physics-informed Neural Time Fields for Prehensile Object Manipulation Sampling-based algorithms for optimal motion planning

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T04:51:49.454994Z

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.

Pith citing papers

No inbound Pith citation observations are available.