Paper Citation Record · LEDGER

ELMP: Efficient Learning for Motion Planning via Analytical Policy Gradients

As of 7 August 2026, this Paper Citation Record lists 34 of 34 outbound references and 0 inbound Pith citation observations for arXiv:2607.00215.

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

pith.paper-citation-record.v1
2607.00215 v1

Coverage vector

measured 34 of 34 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-02T18:27:35.055003Z

measured 34 of 34 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-06T06:34:29.942622+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

34 of 34 outbound references displayed

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verified fuzzy28
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation f81d1449-9878-4da6-9566-ad7d83c4ae54 · outbound

This paper cites Motion policy networks,.

ELMP: Efficient Learning for Motion Planning via Analytical Policy Gradients Motion policy networks,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T21:51:27.357305Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 05722432-271b-4888-9595-5d5dcab2aa17 · outbound

This paper cites Neural MP: A Generalist Neural Motion Planner.

ELMP: Efficient Learning for Motion Planning via Analytical Policy Gradients Neural MP: A Generalist Neural Motion Planner

Reference 2

Resolution: verified exact; arxiv_id, observed 2026-07-02T18:37:16.336840Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 9d7bdc4e-2d9b-4f8e-9d4b-54af81a97ce7 · outbound

This paper cites Deep Reactive Policy: Learning Reactive Manipulator Motion Planning for Dynamic Environments.

ELMP: Efficient Learning for Motion Planning via Analytical Policy Gradients Deep Reactive Policy: Learning Reactive Manipulator Motion Planning for Dynamic Environments

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-07-02T18:37:16.340586Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 52219759-6f93-4d25-a1c0-c7b830b6bf7b · outbound

This paper cites Rapidly-exploring random trees: A new tool for path planning.

ELMP: Efficient Learning for Motion Planning via Analytical Policy Gradients Rapidly-exploring random trees: A new tool for path planning

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T21:51:27.360122Z

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Observation db860789-17f8-4f78-af1c-d45b976007c2 · outbound

This paper cites Prob- abilistic roadmaps for path planning in high-dimensional configuration spaces,.

ELMP: Efficient Learning for Motion Planning via Analytical Policy Gradients Prob- abilistic roadmaps for path planning in high-dimensional configuration spaces,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T21:51:27.368799Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 78136709-2efd-448a-b6bd-68bfa2dc403d · outbound

This paper cites Path planning using lazy prm.

ELMP: Efficient Learning for Motion Planning via Analytical Policy Gradients Path planning using lazy prm

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T21:51:27.366564Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 4ccda60e-8648-4127-8b52-4cc190736b42 · outbound

This paper cites Batch informed trees (bit*): Informed asymptotically optimal anytime search.

ELMP: Efficient Learning for Motion Planning via Analytical Policy Gradients Batch informed trees (bit*): Informed asymptotically optimal anytime search

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T21:51:27.371235Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation a83e154f-7108-4d20-88ce-dc9f70aa1f06 · outbound

This paper cites Adaptively informed trees (ait*): Fast asymptotically optimal path planning through adaptive heuristics,.

ELMP: Efficient Learning for Motion Planning via Analytical Policy Gradients Adaptively informed trees (ait*): Fast asymptotically optimal path planning through adaptive heuristics,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T21:51:27.362790Z

Source-reported events for the cited work

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Observation 4967749a-9da8-4e7c-a72e-ea6dc48eb834 · outbound

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

ELMP: Efficient Learning for Motion Planning via Analytical Policy Gradients Chomp: Gradient optimization techniques for efficient motion planning,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T21:51:27.349359Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 18c10f09-ecfc-4694-90c3-5596e6d93f8e · outbound

This paper cites Motion planning with sequential convex optimization and convex collision checking.

ELMP: Efficient Learning for Motion Planning via Analytical Policy Gradients Motion planning with sequential convex optimization and convex collision checking

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T21:51:27.340815Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation d02301e9-9e61-4791-848e-8fd6016e276c · outbound

This paper cites The convex feasible set algo- rithm for real time optimization in motion planning.

ELMP: Efficient Learning for Motion Planning via Analytical Policy Gradients The convex feasible set algo- rithm for real time optimization in motion planning

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T21:51:27.399680Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation cf2e54a8-69b5-42f5-9c9c-a5c93f6e50f3 · outbound

This paper cites Curobo: Parallelized collision-free robot motion generation.

ELMP: Efficient Learning for Motion Planning via Analytical Policy Gradients Curobo: Parallelized collision-free robot motion generation

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T21:51:27.373578Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 6ef0a8ef-96b0-40e9-84c8-5b6facf5cd67 · outbound

This paper cites Storm: An integrated framework for fast joint-space model-predictive control for reactive manipulation,.

ELMP: Efficient Learning for Motion Planning via Analytical Policy Gradients Storm: An integrated framework for fast joint-space model-predictive control for reactive manipulation,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T21:51:27.361978Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 53442e85-74ed-47d5-9fef-8a9d010680fb · outbound

This paper cites Geometric fabrics: Generalizing classical mechanics to capture the physics of behavior,.

ELMP: Efficient Learning for Motion Planning via Analytical Policy Gradients Geometric fabrics: Generalizing classical mechanics to capture the physics of behavior,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T21:51:27.373211Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation e7561cf1-c3a1-44a1-9da8-38ec0ebfa448 · outbound

This paper cites Learning fast, tool- aware collision avoidance for collaborative robots,.

ELMP: Efficient Learning for Motion Planning via Analytical Policy Gradients Learning fast, tool- aware collision avoidance for collaborative robots,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T21:51:27.371093Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation e831828c-7518-4b2c-821d-f046f1b3412d · outbound

This paper cites Motion planning networks: Bridging the gap between learning-based and classical motion planners,.

ELMP: Efficient Learning for Motion Planning via Analytical Policy Gradients Motion planning networks: Bridging the gap between learning-based and classical motion planners,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T21:51:27.404959Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 9aee6d0a-424a-4f11-9c84-eda870c3cc46 · outbound

This paper cites Brax-a differentiable physics engine for large scale rigid body simulation, 2021,.

ELMP: Efficient Learning for Motion Planning via Analytical Policy Gradients Brax-a differentiable physics engine for large scale rigid body simulation, 2021,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T21:51:27.395826Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation bf651a73-43a5-44c7-ae7f-60de48287e16 · outbound

This paper cites Newton: GPU-accelerated physics simulation for robotics, and simulation research.

ELMP: Efficient Learning for Motion Planning via Analytical Policy Gradients Newton: GPU-accelerated physics simulation for robotics, and simulation research

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T21:51:27.397851Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation bdf8ce94-70ad-4a1c-ac00-5279aaf57276 · outbound

This paper cites Learning on the fly: Rapid policy adaptation via differentiable simu- lation,.

ELMP: Efficient Learning for Motion Planning via Analytical Policy Gradients Learning on the fly: Rapid policy adaptation via differentiable simu- lation,

Reference 19

Resolution: verified exact; arxiv_id, observed 2026-07-02T18:37:16.324117Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation e484343f-2dc0-412a-95ef-9b01db79258b · outbound

This paper cites Training efficient controllers via analytic policy gradient,.

ELMP: Efficient Learning for Motion Planning via Analytical Policy Gradients Training efficient controllers via analytic policy gradient,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T21:51:27.389849Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 29bcda79-c3db-42d3-93af-bf983e2a68c4 · outbound

This paper cites Differentiable composite neural signed distance fields for navigation in dynamic indoor scenes,.

ELMP: Efficient Learning for Motion Planning via Analytical Policy Gradients Differentiable composite neural signed distance fields for navigation in dynamic indoor scenes,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T21:51:27.391986Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation f454f395-a264-4d09-81a9-9a9ab3086cb0 · outbound

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

ELMP: Efficient Learning for Motion Planning via Analytical Policy Gradients Pointnet++: Deep hierarchical feature learning on point sets in a metric space

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T21:51:27.387729Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation c8da32b6-5e1d-40d7-aca4-28818ee5b81b · outbound

This paper cites Backpropagation through time: what it does and how to do it,.

ELMP: Efficient Learning for Motion Planning via Analytical Policy Gradients Backpropagation through time: what it does and how to do it,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T21:51:27.383571Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation e86446d2-ccd4-482b-82e1-d5d48c28585a · outbound

This paper cites A Smooth Representation of Belief over SO(3) for Deep Rotation Learning with Uncertainty.

ELMP: Efficient Learning for Motion Planning via Analytical Policy Gradients A Smooth Representation of Belief over SO(3) for Deep Rotation Learning with Uncertainty

Reference 24

Resolution: metadata mismatch; arxiv_id, observed 2026-07-02T18:37:16.327470Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation a711d9df-b789-49f7-b4bd-0c75d640e15c · outbound

This paper cites Learning with 3D rotations, a hitchhiker's guide to SO(3).

ELMP: Efficient Learning for Motion Planning via Analytical Policy Gradients Learning with 3D rotations, a hitchhiker's guide to SO(3)

Reference 25

Resolution: verified exact; arxiv_id, observed 2026-07-02T18:37:16.333523Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 03824b9b-0071-43d6-bf33-e483051134c3 · outbound

This paper cites On the analysis of movement smoothness,.

ELMP: Efficient Learning for Motion Planning via Analytical Policy Gradients On the analysis of movement smoothness,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T21:51:27.385867Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 226cf401-87f9-4c3d-a3b4-111cb927e248 · outbound

This paper cites Learning long-term dependen- cies with gradient descent is difficult,.

ELMP: Efficient Learning for Motion Planning via Analytical Policy Gradients Learning long-term dependen- cies with gradient descent is difficult,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T21:51:27.394015Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 6b3aeceb-0883-4313-82f4-e19289d392e1 · outbound

This paper cites On the difficulty of training recurrent neural networks,.

ELMP: Efficient Learning for Motion Planning via Analytical Policy Gradients On the difficulty of training recurrent neural networks,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T21:51:27.401462Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 23682b6d-7faf-43ff-b6ba-bee27bc2ec15 · outbound

This paper cites Gradients are Not All You Need.

ELMP: Efficient Learning for Motion Planning via Analytical Policy Gradients Gradients are Not All You Need

Reference 29

Resolution: verified exact; arxiv_id, observed 2026-07-02T18:37:16.333395Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation a1836fca-dfa3-44f9-9b48-b420c49afb12 · outbound

This paper cites realtime urdf filter,.

ELMP: Efficient Learning for Motion Planning via Analytical Policy Gradients realtime urdf filter,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T21:51:27.403065Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation c6600b17-03e9-480b-a255-842fbbb5a18f · outbound

This paper cites Learning by cheating,.

ELMP: Efficient Learning for Motion Planning via Analytical Policy Gradients Learning by cheating,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T21:51:27.354103Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 6952de46-eddb-4c48-a9e0-f5f0a55b9764 · outbound

This paper cites Learning quadrupedal locomotion over challenging terrain.

ELMP: Efficient Learning for Motion Planning via Analytical Policy Gradients Learning quadrupedal locomotion over challenging terrain

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T21:51:27.378368Z

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No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 39315872-8d16-4201-be36-e3567f6912fd · outbound

This paper cites Accelerated policy learning with parallel differen- tiable simulation,.

ELMP: Efficient Learning for Motion Planning via Analytical Policy Gradients Accelerated policy learning with parallel differen- tiable simulation,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T21:51:27.380727Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 51bc5c8c-8cab-4543-a82d-21824d193d94 · outbound

This paper cites A new approach to time-optimal path parameterization based on reachability analysis.

ELMP: Efficient Learning for Motion Planning via Analytical Policy Gradients A new approach to time-optimal path parameterization based on reachability analysis

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-07-05T21:51:27.375762Z

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No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Pith citing papers

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