Pith. sign in

Paper Citation Record · LEDGER

GASP: GPU-Accelerated Safe Planner for Real-Time Collision-Aware Motion Generation with Latent Trajectory Sampling

As of 7 August 2026, this Paper Citation Record lists 19 of 19 outbound references and 0 inbound Pith citation observations for arXiv:2608.04612.

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

pith.paper-citation-record.v1
2608.04612 v1

Coverage vector

measured 19 of 19 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T20:50:11.933800Z

measured 19 of 19 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+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

19 of 19 outbound references displayed

  • verified exact1
  • verified fuzzy8
  • unresolved10
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 411f3068-df21-4009-9480-bf4cf1642d93 · outbound

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

GASP: GPU-Accelerated Safe Planner for Real-Time Collision-Aware Motion Generation with Latent Trajectory Sampling Progressive Learning for Physics-informed Neural Motion Planning

Reference 1

Resolution
unresolved
no resolver link, observed 2026-08-06T20:50:10.903679Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:50:10.903679Z digest=sha256:f365e92967488f24e7a1e19042171795d5247c11e27331c4b149e4ca67318bd0

Observation 18a3a329-ca76-4c04-b7d4-04026e9ad2b1 · outbound

This paper cites Fast Kinodynamic Planning on the Constraint Manifold with Deep Neural Networks.

GASP: GPU-Accelerated Safe Planner for Real-Time Collision-Aware Motion Generation with Latent Trajectory Sampling Fast Kinodynamic Planning on the Constraint Manifold with Deep Neural Networks

Reference 2

Resolution
verified exact
local_arxiv, observed 2026-08-06T20:50:12.113492Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T20:50:10.975132Z digest=sha256:8158d2407abec099e05ef01d6b90487d5cb47b12248c8edd41b599f55ab12ebf

Observation c0a0c329-d168-469c-ac06-a6ac53744ad9 · outbound

This paper cites DiffusionSeeder: Seeding Motion Optimization with Diffusion for Rapid Motion Planning.

GASP: GPU-Accelerated Safe Planner for Real-Time Collision-Aware Motion Generation with Latent Trajectory Sampling DiffusionSeeder: Seeding Motion Optimization with Diffusion for Rapid Motion Planning

Reference 3

Resolution
unresolved
no resolver link, observed 2026-08-06T20:50:11.088891Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:50:11.088891Z digest=sha256:28b1d62944372dbba4720c13834c147320b97be35fdeb9a40f0f7719148aa6e8

Observation 6f022b49-bb8b-4a64-ba4b-48bdb5456236 · outbound

This paper cites Learning to Walk in Minutes Using Massively Parallel Deep Reinforcement Learning.

GASP: GPU-Accelerated Safe Planner for Real-Time Collision-Aware Motion Generation with Latent Trajectory Sampling Learning to Walk in Minutes Using Massively Parallel Deep Reinforcement Learning

Reference 4

Resolution
unresolved
no resolver link, observed 2026-08-06T20:50:11.167208Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:50:11.167208Z digest=sha256:1d952a33b4058f260a1392c620cc2e8fbb82167a78c259a539c50287c9fc50d4

Observation 0fcf4eee-3e2a-4864-a27a-e5ccdd39a6ec · outbound

This paper cites Fast kinodynamic planning on the constraint manifold with deep neural networks,.

GASP: GPU-Accelerated Safe Planner for Real-Time Collision-Aware Motion Generation with Latent Trajectory Sampling Fast kinodynamic planning on the constraint manifold with deep neural networks,

Reference 5

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:50:13.058656Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T20:50:11.276733Z digest=sha256:fb4ffceb1bc27fb33ba7f41ed8be727235cc371d5605e190a96f299b206c11ce

Observation e49da02e-9155-4cec-b93b-6b704a941810 · outbound

This paper cites cuRoboV2: Dynamics-Aware Motion Generation with Depth-Fused Distance Fields for High-DoF Robots.

GASP: GPU-Accelerated Safe Planner for Real-Time Collision-Aware Motion Generation with Latent Trajectory Sampling cuRoboV2: Dynamics-Aware Motion Generation with Depth-Fused Distance Fields for High-DoF Robots

Reference 6

Resolution
unresolved
no resolver link, observed 2026-08-06T20:50:11.362387Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:50:11.362387Z digest=sha256:2879167b69f0aeef09c50d2eaa0fdb85d11ecdbf67d83099d034a621062e311c

Observation 8f5b44f8-571b-4ee3-bbad-f8f200e2e150 · outbound

This paper cites Speeding up deep neural network- based planning of local car maneuvers via efficient b-spline path construction,.

GASP: GPU-Accelerated Safe Planner for Real-Time Collision-Aware Motion Generation with Latent Trajectory Sampling Speeding up deep neural network- based planning of local car maneuvers via efficient b-spline path construction,

Reference 7

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:50:13.049384Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T20:50:11.451848Z digest=sha256:3b4fb25f76e6c3325a1a29ce4b430f61135bd8603bcf5e30aa62aab18cd5ac0d

Observation 7a941ec7-127b-44fd-9bc2-bde6aa427b01 · outbound

This paper cites Jerk-limited Real-time Trajectory Generation with Arbitrary Target States.

GASP: GPU-Accelerated Safe Planner for Real-Time Collision-Aware Motion Generation with Latent Trajectory Sampling Jerk-limited Real-time Trajectory Generation with Arbitrary Target States

Reference 8

Resolution
unresolved
no resolver link, observed 2026-08-06T20:50:11.525934Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:50:11.525934Z digest=sha256:f19b22f471697513bb5ca2ebf0dca68068a86c83a0616fc9ef2db43177e05271

Observation e5e5d6d7-edd4-4c05-80cf-0df357e61dfa · outbound

This paper cites cuRobo: Parallelized Collision-Free Minimum-Jerk Robot Motion Generation.

GASP: GPU-Accelerated Safe Planner for Real-Time Collision-Aware Motion Generation with Latent Trajectory Sampling cuRobo: Parallelized Collision-Free Minimum-Jerk Robot Motion Generation

Reference 9

Resolution
unresolved
no resolver link, observed 2026-08-06T20:50:11.574674Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:50:11.574674Z digest=sha256:f0f2b0e504f4875b88a35839fb5d37b15c956803677853f2c031ce05635ec209

Observation 4cc0abae-af76-41b4-a99f-8340d4e1829e · outbound

This paper cites Chomp: Gradient optimization techniques for efficient motion planning,.

GASP: GPU-Accelerated Safe Planner for Real-Time Collision-Aware Motion Generation with Latent Trajectory Sampling Chomp: Gradient optimization techniques for efficient motion planning,

Reference 10

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:50:12.955557Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T20:50:11.602365Z digest=sha256:92247eb9eaa7cb6a7d860d3fcfbf450242f968bec261340d1c282e5df13d82c8

Observation 228bedec-2e8f-4a64-be51-d928f466bea3 · outbound

This paper cites G-mapp: Gpu-accelerated multi-agent planning and perception for reactive motion generation,.

GASP: GPU-Accelerated Safe Planner for Real-Time Collision-Aware Motion Generation with Latent Trajectory Sampling G-mapp: Gpu-accelerated multi-agent planning and perception for reactive motion generation,

Reference 11

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:50:12.932837Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T20:50:11.649990Z digest=sha256:0d415d373568a3f4697bb3b957a709d51be2462a7c4ba98f02611d58bc69458f

Observation 50b9e79f-2a3f-4c3c-af06-2520a8c3dedd · outbound

This paper cites Motion planning diffusion: Learning and adapting robot motion planning with diffusion models,.

GASP: GPU-Accelerated Safe Planner for Real-Time Collision-Aware Motion Generation with Latent Trajectory Sampling Motion planning diffusion: Learning and adapting robot motion planning with diffusion models,

Reference 12

Resolution
unresolved
no resolver link, observed 2026-08-06T20:50:11.692357Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:50:11.692357Z digest=sha256:c8f22ac58d8858524260b09417593d8fa8cb861971f9a84b973c6940c863ceac

Observation 5869d352-9738-41ae-8acc-8e07dbfb20cf · outbound

This paper cites Flow Matching Policy Gradients.

GASP: GPU-Accelerated Safe Planner for Real-Time Collision-Aware Motion Generation with Latent Trajectory Sampling Flow Matching Policy Gradients

Reference 13

Resolution
unresolved
no resolver link, observed 2026-08-06T20:50:11.728877Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:50:11.728877Z digest=sha256:a7ef38a4f2e8a7a11f71defbf4bd9c4edcb42233394995cb1b63d93ee7c16c66

Observation a4fbc34b-a807-403d-83c9-92f958b30084 · outbound

This paper cites Outplaying elite table tennis players with an au- tonomous robot,.

GASP: GPU-Accelerated Safe Planner for Real-Time Collision-Aware Motion Generation with Latent Trajectory Sampling Outplaying elite table tennis players with an au- tonomous robot,

Reference 14

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:50:12.770717Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T20:50:11.898279Z digest=sha256:743193384fe990f62addc4d5ca22872a81297dd2ab547ad27b39e8e61945eebd

Observation 82495025-9154-4d8e-97a5-4bcfa73e4927 · outbound

This paper cites Tra- jectories are then decoded by batched matrix multiplication.

GASP: GPU-Accelerated Safe Planner for Real-Time Collision-Aware Motion Generation with Latent Trajectory Sampling Tra- jectories are then decoded by batched matrix multiplication

Reference 15

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:50:12.613741Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T20:50:11.913338Z digest=sha256:377f0f4391a5f362d53524820510a4fccab59fc113bde107bb5eb7f995e02055

Observation 0edd695e-31d7-4fb0-b056-3a5f92a01739 · outbound

This paper cites The default minibatch size is64; because samples are generated online, each epoch is defined as256optimizer steps, with validation every epoch on four batches of size256.

GASP: GPU-Accelerated Safe Planner for Real-Time Collision-Aware Motion Generation with Latent Trajectory Sampling The default minibatch size is64; because samples are generated online, each epoch is defined as256optimizer steps, with validation every epoch on four batches of size256

Reference 16

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:50:12.478213Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T20:50:11.916290Z digest=sha256:a5cf6772f61c179b9e44026a0d361bd7605590675000d887c2d258520d000d07

Observation 8c897c0b-4b0b-4d1b-9f71-fd49815692cd · outbound

This paper cites an unresolved cited work.

GASP: GPU-Accelerated Safe Planner for Real-Time Collision-Aware Motion Generation with Latent Trajectory Sampling Unresolved cited work

Reference 17

Resolution
unresolved
raw_fallback, observed 2026-08-06T20:50:12.433007Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T20:50:11.918955Z digest=sha256:21f2e26f8cf4752acea214a9e2f8b964f7ab64b3427cbbe3907249c077b72f96

Observation 9b6de909-7eda-4fa3-a753-a7f35b71b4ec · outbound

This paper cites Several losses are evaluated only on the learnable interior part of the spline, namely betweenc 2 andc nc−2 as defined in 6.

GASP: GPU-Accelerated Safe Planner for Real-Time Collision-Aware Motion Generation with Latent Trajectory Sampling Several losses are evaluated only on the learnable interior part of the spline, namely betweenc 2 andc nc−2 as defined in 6

Reference 18

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:50:12.402910Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T20:50:11.921556Z digest=sha256:ef09249f527255524ac98f9159ab9e22fd52340385740bb88dfdebd5299e3aaa

Observation e2756483-b018-49fd-aa6f-d4034025c3c8 · outbound

This paper cites an unresolved cited work.

GASP: GPU-Accelerated Safe Planner for Real-Time Collision-Aware Motion Generation with Latent Trajectory Sampling Unresolved cited work

Reference 19

Resolution
unresolved
raw_fallback, observed 2026-08-06T20:50:12.249466Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T20:50:11.933800Z digest=sha256:974d8708789217f1a226df282b9c513bb7fd12f0a727ee8fa92e7623611ddbfe

Pith citing papers

No inbound Pith citation observations are available.