Paper Citation Record · LEDGER

Investigating Lagrangian Neural Networks for Infinite Horizon Planning in Quadrupedal Locomotion

As of 10 August 2026, this Paper Citation Record lists 30 of 30 outbound references and 0 inbound Pith citation observations for arXiv:2506.16079.

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

pith.paper-citation-record.v1
2506.16079 v1

Coverage vector

measured 30 of 30 reference resolution

Typed states for the displayed outbound observations.

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Pith citing papers itemized under the disclosed page cap.

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

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Reference resolution

30 of 30 outbound references displayed

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External citation measurements

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Outbound references

Observation 7d32ef11-389e-4f51-a3ea-7c0a331df7ed · outbound

This paper cites Robot dynamics and control,.

Investigating Lagrangian Neural Networks for Infinite Horizon Planning in Quadrupedal Locomotion Robot dynamics and control,

Reference 1

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Observation cccac2de-5e1b-4ddd-a062-e572aee5d786 · outbound

This paper cites Neural ordinary differential equations,.

Investigating Lagrangian Neural Networks for Infinite Horizon Planning in Quadrupedal Locomotion Neural ordinary differential equations,

Reference 2

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Observation fd6881c0-aa4c-4290-8d04-a93a321d337b · outbound

This paper cites Physics Informed Deep Learning (Part I): Data-driven Solutions of Nonlinear Partial Differential Equations.

Investigating Lagrangian Neural Networks for Infinite Horizon Planning in Quadrupedal Locomotion Physics Informed Deep Learning (Part I): Data-driven Solutions of Nonlinear Partial Differential Equations

Reference 3

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Observation 87a73e88-a921-428f-9fed-bb2773d9883f · outbound

This paper cites Physics Informed Deep Learning (Part II): Data-driven Discovery of Nonlinear Partial Differential Equations.

Investigating Lagrangian Neural Networks for Infinite Horizon Planning in Quadrupedal Locomotion Physics Informed Deep Learning (Part II): Data-driven Discovery of Nonlinear Partial Differential Equations

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-06T23:49:27.263026Z

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Observation 42525beb-64eb-4752-be81-7d73e72688ca · outbound

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

Investigating Lagrangian Neural Networks for Infinite Horizon Planning in Quadrupedal Locomotion Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations,

Reference 5

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Observation 57fa11a3-ad19-4970-a59d-856fe84a482f · outbound

This paper cites Neural net- works with physics-informed architectures and constraints for dynamical systems modeling,.

Investigating Lagrangian Neural Networks for Infinite Horizon Planning in Quadrupedal Locomotion Neural net- works with physics-informed architectures and constraints for dynamical systems modeling,

Reference 6

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Observation 6b3b7e8f-7173-43a7-9f7b-6125883d8996 · outbound

This paper cites Lagrangian Neural Networks.

Investigating Lagrangian Neural Networks for Infinite Horizon Planning in Quadrupedal Locomotion Lagrangian Neural Networks

Reference 7

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Observation 9df75873-0d32-4398-8946-b583c0503d40 · outbound

This paper cites Simplifying hamiltonian and lagrangian neural networks via explicit constraints,.

Investigating Lagrangian Neural Networks for Infinite Horizon Planning in Quadrupedal Locomotion Simplifying hamiltonian and lagrangian neural networks via explicit constraints,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:49:29.573725Z

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Observation a6d99d79-afb5-4144-a5ab-074ec4959015 · outbound

This paper cites Deconstructing the inductive biases of hamiltonian neural networks,.

Investigating Lagrangian Neural Networks for Infinite Horizon Planning in Quadrupedal Locomotion Deconstructing the inductive biases of hamiltonian neural networks,

Reference 9

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Observation 1b9fd4c5-48ce-47d7-8463-ccf0b7f8389e · outbound

This paper cites Simplifying hamiltonian and lagrangian neural networks via explicit constraints,.

Investigating Lagrangian Neural Networks for Infinite Horizon Planning in Quadrupedal Locomotion Simplifying hamiltonian and lagrangian neural networks via explicit constraints,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:49:29.538710Z

Source-reported events for the cited work

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Observation 63f9b106-d994-4176-ba6e-9f7deacc1448 · outbound

This paper cites Deep lagrangian networks: Using physics as model prior for deep learning,.

Investigating Lagrangian Neural Networks for Infinite Horizon Planning in Quadrupedal Locomotion Deep lagrangian networks: Using physics as model prior for deep learning,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:49:29.520470Z

Source-reported events for the cited work

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Observation ea646fc9-af4d-450f-8a49-cf252f6081d6 · outbound

This paper cites Plan Online, Learn Offline: Efficient Learning and Exploration via Model-Based Control.

Investigating Lagrangian Neural Networks for Infinite Horizon Planning in Quadrupedal Locomotion Plan Online, Learn Offline: Efficient Learning and Exploration via Model-Based Control

Reference 12

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Observation 446e9a62-d5d1-4ffc-8145-7fa4350e3c30 · outbound

This paper cites Learning off-policy with online planning,.

Investigating Lagrangian Neural Networks for Infinite Horizon Planning in Quadrupedal Locomotion Learning off-policy with online planning,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:49:29.501863Z

Source-reported events for the cited work

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Observation 4e7cd083-61e8-4fc0-b336-b723f7fc563a · outbound

This paper cites Making better decision by directly planning in continuous control,.

Investigating Lagrangian Neural Networks for Infinite Horizon Planning in Quadrupedal Locomotion Making better decision by directly planning in continuous control,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:49:29.483331Z

Source-reported events for the cited work

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Observation 5e19c998-f776-4ed1-a327-762c71932c92 · outbound

This paper cites Model predictive actor-critic: Accelerating robot skill ac- quisition with deep reinforcement learning,.

Investigating Lagrangian Neural Networks for Infinite Horizon Planning in Quadrupedal Locomotion Model predictive actor-critic: Accelerating robot skill ac- quisition with deep reinforcement learning,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:49:29.464899Z

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Observation eca760a7-d131-4587-9423-58907a14389b · outbound

This paper cites Dynamic mirror descent based model predictive control for accelerating robot learning,.

Investigating Lagrangian Neural Networks for Infinite Horizon Planning in Quadrupedal Locomotion Dynamic mirror descent based model predictive control for accelerating robot learning,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:49:29.446735Z

Source-reported events for the cited work

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Observation ffd82329-753f-467b-93d4-035e47ca5a57 · outbound

This paper cites Temporal Difference Learning for Model Predictive Control.

Investigating Lagrangian Neural Networks for Infinite Horizon Planning in Quadrupedal Locomotion Temporal Difference Learning for Model Predictive Control

Reference 17

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Observation acfc1677-5bbf-4d30-94c7-269d947a34ae · outbound

This paper cites TD-MPC2: Scalable, Robust World Models for Continuous Control.

Investigating Lagrangian Neural Networks for Infinite Horizon Planning in Quadrupedal Locomotion TD-MPC2: Scalable, Robust World Models for Continuous Control

Reference 18

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Observation 4b88f387-133d-4a77-b5d4-389ff578c066 · outbound

This paper cites Pip-loco: A proprioceptive infinite horizon planning framework for quadrupedal robot locomotion,.

Investigating Lagrangian Neural Networks for Infinite Horizon Planning in Quadrupedal Locomotion Pip-loco: A proprioceptive infinite horizon planning framework for quadrupedal robot locomotion,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:49:29.429197Z

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Observation 308e9663-c4b5-4031-b939-2d5d3d336578 · outbound

This paper cites Model predictive path integral control using covariance variable importance sampling,.

Investigating Lagrangian Neural Networks for Infinite Horizon Planning in Quadrupedal Locomotion Model predictive path integral control using covariance variable importance sampling,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:49:29.412241Z

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Observation 0ca61c6e-9360-431e-9cb0-60df4aace3ad · outbound

This paper cites Asymmetric Actor Critic for Image-Based Robot Learning.

Investigating Lagrangian Neural Networks for Infinite Horizon Planning in Quadrupedal Locomotion Asymmetric Actor Critic for Image-Based Robot Learning

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-06T23:49:29.073275Z

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Observation 66ddfa6c-bfb2-4748-8fa6-738d9bdcbc09 · outbound

This paper cites Proximal Policy Optimization Algorithms.

Investigating Lagrangian Neural Networks for Infinite Horizon Planning in Quadrupedal Locomotion Proximal Policy Optimization Algorithms

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-06T23:49:29.078627Z

Source-reported events for the cited work

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Observation 6e6efedc-5ccd-4288-87b4-f22c4ac2382e · outbound

This paper cites Learning agile and dynamic motor skills for legged robots,.

Investigating Lagrangian Neural Networks for Infinite Horizon Planning in Quadrupedal Locomotion Learning agile and dynamic motor skills for legged robots,

Reference 23

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Observation 6c7e4354-3dc8-4e9b-a7c4-218d58679c82 · outbound

This paper cites Barrier functions inspired reward shaping for reinforcement learning,.

Investigating Lagrangian Neural Networks for Infinite Horizon Planning in Quadrupedal Locomotion Barrier functions inspired reward shaping for reinforcement learning,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:49:29.383849Z

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Observation 7934b54f-d447-4f35-9c14-8f3feecce849 · outbound

This paper cites Dreamwaq: Learning robust quadrupedal locomotion with implicit terrain imagination via deep reinforcement learning,.

Investigating Lagrangian Neural Networks for Infinite Horizon Planning in Quadrupedal Locomotion Dreamwaq: Learning robust quadrupedal locomotion with implicit terrain imagination via deep reinforcement learning,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:49:29.366634Z

Source-reported events for the cited work

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Observation c3c2b291-1a31-4cc8-8b1e-467f3fc1cb2b · outbound

This paper cites Hybrid internal model: Learning agile legged locomotion with simulated robot response,.

Investigating Lagrangian Neural Networks for Infinite Horizon Planning in Quadrupedal Locomotion Hybrid internal model: Learning agile legged locomotion with simulated robot response,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:49:29.347952Z

Source-reported events for the cited work

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Observation 9192a6df-a7d3-4da9-bdfb-7f77ea2e45ff · outbound

This paper cites Isaac gym: High performance gpu-based physics simulation for robot learning,.

Investigating Lagrangian Neural Networks for Infinite Horizon Planning in Quadrupedal Locomotion Isaac gym: High performance gpu-based physics simulation for robot learning,

Reference 27

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Observation 197f93e6-230d-45a7-973c-acfdf3b32260 · outbound

This paper cites Learning to walk in minutes using massively parallel deep reinforcement learning,.

Investigating Lagrangian Neural Networks for Infinite Horizon Planning in Quadrupedal Locomotion Learning to walk in minutes using massively parallel deep reinforcement learning,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:49:29.320284Z

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Observation ca90192f-ed7a-422d-8764-dfe3861782f2 · outbound

This paper cites Automatic differentiation in pytorch,.

Investigating Lagrangian Neural Networks for Infinite Horizon Planning in Quadrupedal Locomotion Automatic differentiation in pytorch,

Reference 29

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Source-reported events for the cited work

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Observation 7b275b48-0940-4dc0-88de-d6e87928f976 · outbound

This paper cites JAX: composable transformations of Python+NumPy pro- grams,.

Investigating Lagrangian Neural Networks for Infinite Horizon Planning in Quadrupedal Locomotion JAX: composable transformations of Python+NumPy pro- grams,

Reference 30

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