Paper Citation Record · LEDGER

Learning Predictive Control with Deep Koopman Operators for Autonomous Vehicle Motion Planning

As of 5 August 2026, this Paper Citation Record lists 46 of 46 outbound references and 2 inbound Pith citation observations for arXiv:2606.08136.

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

pith.paper-citation-record.v1
2606.08136 v1

Coverage vector

measured 46 of 46 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-06-27T19:28:52.968579Z

measured 48 of 48 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-05T06:32:48.257954+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-04T01:39:44.467388Z

measured 1 of 1 external citation measurements

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

Source: pith, observed 2026-08-05T02:28:24.338817Z

Reference resolution

46 of 46 outbound references displayed

verified exact1
verified fuzzy0
unresolved45
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malformed identifier0
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External citation measurements

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pith, observed 2026-08-05T02:28:24.338817Z

Outbound references

Observation 0fe931b7-29db-4a7e-8291-23e4d22dc88a · outbound

This paper cites Receding-horizon reinforcement learning approach for kinodynamic motion planning of autonomous vehicles.IEEE Transactions on Intelligent Vehicles, 7(3):556–568, 2022.

Learning Predictive Control with Deep Koopman Operators for Autonomous Vehicle Motion Planning Receding-horizon reinforcement learning approach for kinodynamic motion planning of autonomous vehicles.IEEE Transactions on Intelligent Vehicles, 7(3):556–568, 2022

Reference 1

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Observation d47c5544-5b12-4e5a-bc89-c6b81b137656 · outbound

This paper cites The convex feasible set algorithm for real time optimization in motion planning.

Learning Predictive Control with Deep Koopman Operators for Autonomous Vehicle Motion Planning The convex feasible set algorithm for real time optimization in motion planning

Reference 2

Resolution: unresolved; no resolver link, observed 2026-06-27T19:28:52.968579Z

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Observation 52ffacb0-cb9e-4505-8062-b99fe81c996f · outbound

This paper cites A survey of the state- of-the-art localization techniques and their potentials for autonomous vehicle applications.IEEE Internet of Things Journal, 5:829–846, 2018.

Learning Predictive Control with Deep Koopman Operators for Autonomous Vehicle Motion Planning A survey of the state- of-the-art localization techniques and their potentials for autonomous vehicle applications.IEEE Internet of Things Journal, 5:829–846, 2018

Reference 3

Resolution: unresolved; no resolver link, observed 2026-06-27T19:28:52.968579Z

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Observation bc4eda6f-f19b-417a-bd8b-29a13443a82c · outbound

This paper cites Autonomous driving motion planning with constrained iterative lqr.IEEE Transactions on Intelligent Vehicles, 4(2):244–254, 2019.

Learning Predictive Control with Deep Koopman Operators for Autonomous Vehicle Motion Planning Autonomous driving motion planning with constrained iterative lqr.IEEE Transactions on Intelligent Vehicles, 4(2):244–254, 2019

Reference 4

Resolution: unresolved; no resolver link, observed 2026-06-27T19:28:52.968579Z

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Observation 41b900ed-4e48-4d41-80ea-cbb3900f43a6 · outbound

This paper cites Safety-critical model predictive control with discrete-time control barrier function.

Learning Predictive Control with Deep Koopman Operators for Autonomous Vehicle Motion Planning Safety-critical model predictive control with discrete-time control barrier function

Reference 5

Resolution: unresolved; no resolver link, observed 2026-06-27T19:28:52.968579Z

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Observation 39e3c2b7-9368-4a0c-b3fe-408d3e76e0b6 · outbound

This paper cites Model predictive contouring control for collision avoidance in unstructured dy- namic environments.IEEE Robotics and Automation Letters, 4(4):4459– 4466, 2019.

Learning Predictive Control with Deep Koopman Operators for Autonomous Vehicle Motion Planning Model predictive contouring control for collision avoidance in unstructured dy- namic environments.IEEE Robotics and Automation Letters, 4(4):4459– 4466, 2019

Reference 6

Resolution: unresolved; no resolver link, observed 2026-06-27T19:28:52.968579Z

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Observation 5cd62fd7-6a28-4c8b-986e-d1a4e26b1bad · outbound

This paper cites Multi-kernel online reinforcement learning for path tracking control of intelligent vehicles.IEEE Transactions on Systems, Man, and Cybernetics: Systems, 51(11):6962–6975, 2020.

Learning Predictive Control with Deep Koopman Operators for Autonomous Vehicle Motion Planning Multi-kernel online reinforcement learning for path tracking control of intelligent vehicles.IEEE Transactions on Systems, Man, and Cybernetics: Systems, 51(11):6962–6975, 2020

Reference 7

Resolution: unresolved; no resolver link, observed 2026-06-27T19:28:52.968579Z

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Observation 57c491c2-875f-4e61-8d97-c9c540dac766 · outbound

This paper cites Lateral control for autonomous land vehicles via dual heuristic programming.

Learning Predictive Control with Deep Koopman Operators for Autonomous Vehicle Motion Planning Lateral control for autonomous land vehicles via dual heuristic programming

Reference 8

Resolution: unresolved; no resolver link, observed 2026-06-27T19:28:52.968579Z

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Observation 4bacae97-5ee0-40cb-9764-ab9f3cdbc1ed · outbound

This paper cites an unresolved cited work.

Learning Predictive Control with Deep Koopman Operators for Autonomous Vehicle Motion Planning Unresolved cited work

Reference 9

Resolution: unresolved; no resolver link, observed 2026-06-27T19:28:52.968579Z

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Observation facf6c1c-0667-41bc-9869-e7e227b4bd82 · outbound

This paper cites Functional nonlinear model predictive control based on adaptive dynamic program- ming.IEEE transactions on Cybernetics, 49(12):4206–4218, 2018.

Learning Predictive Control with Deep Koopman Operators for Autonomous Vehicle Motion Planning Functional nonlinear model predictive control based on adaptive dynamic program- ming.IEEE transactions on Cybernetics, 49(12):4206–4218, 2018

Reference 10

Resolution: unresolved; no resolver link, observed 2026-06-27T19:28:52.968579Z

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Observation a5a4d885-44d6-405d-bfb8-9728e51f5e99 · outbound

This paper cites Linear predictors for nonlinear dynamical systems: Koopman operator meets model predictive control.Automatica, 93:149–160, 2018.

Learning Predictive Control with Deep Koopman Operators for Autonomous Vehicle Motion Planning Linear predictors for nonlinear dynamical systems: Koopman operator meets model predictive control.Automatica, 93:149–160, 2018

Reference 11

Resolution: unresolved; no resolver link, observed 2026-06-27T19:28:52.968579Z

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Observation 219bc812-5d9f-46af-9f52-9de48bf9a90a · outbound

This paper cites Koopman operator applications in signalized traffic systems.IEEE Transactions on Intelligent Transportation Systems, 23(4):3214–3225, 2020.

Learning Predictive Control with Deep Koopman Operators for Autonomous Vehicle Motion Planning Koopman operator applications in signalized traffic systems.IEEE Transactions on Intelligent Transportation Systems, 23(4):3214–3225, 2020

Reference 12

Resolution: unresolved; no resolver link, observed 2026-06-27T19:28:52.968579Z

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Observation 605fcb88-f6c4-4e5f-877d-39889357d8ed · outbound

This paper cites Physically analyzable ai-based nonlinear platoon dynamics modeling during traffic oscillation: A koopman approach.IEEE Transactions on Intelligent Transportation Systems, 2025.

Learning Predictive Control with Deep Koopman Operators for Autonomous Vehicle Motion Planning Physically analyzable ai-based nonlinear platoon dynamics modeling during traffic oscillation: A koopman approach.IEEE Transactions on Intelligent Transportation Systems, 2025

Reference 13

Resolution: unresolved; no resolver link, observed 2026-06-27T19:28:52.968579Z

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Observation 70b22053-009f-497d-b6f5-38f42a0f7c9e · outbound

This paper cites Differential high order control barrier function- based safe reinforcement learning.IEEE Robotics and Automation Letters, 2025.

Learning Predictive Control with Deep Koopman Operators for Autonomous Vehicle Motion Planning Differential high order control barrier function- based safe reinforcement learning.IEEE Robotics and Automation Letters, 2025

Reference 14

Resolution: unresolved; no resolver link, observed 2026-06-27T19:28:52.968579Z

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Observation 99815cf9-35e8-408c-91e0-55f8893933b5 · outbound

This paper cites Cbf-based hierar- chical quadratic programs with guaranteed feasibility for safety-critical systems.IEEE Transactions on Automation Science and Engineering, 22:23687–23699, 2025.

Learning Predictive Control with Deep Koopman Operators for Autonomous Vehicle Motion Planning Cbf-based hierar- chical quadratic programs with guaranteed feasibility for safety-critical systems.IEEE Transactions on Automation Science and Engineering, 22:23687–23699, 2025

Reference 15

Resolution: unresolved; no resolver link, observed 2026-06-27T19:28:52.968579Z

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Observation df3aefec-8ee6-416e-9229-5666f3f33cd4 · outbound

This paper cites Safe and fast tracking on a robot manipulator: Robust MPC and neural network control.IEEE Robotics and Automation Letters, 5(2):3050–3057, 2020.

Learning Predictive Control with Deep Koopman Operators for Autonomous Vehicle Motion Planning Safe and fast tracking on a robot manipulator: Robust MPC and neural network control.IEEE Robotics and Automation Letters, 5(2):3050–3057, 2020

Reference 16

Resolution: unresolved; no resolver link, observed 2026-06-27T19:28:52.968579Z

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Observation dc7ef9a8-6945-4981-b520-f1d3ac3feaf0 · outbound

This paper cites MPC-based haptic shared steering system: A driver modeling approach for symbiotic driving.

Learning Predictive Control with Deep Koopman Operators for Autonomous Vehicle Motion Planning MPC-based haptic shared steering system: A driver modeling approach for symbiotic driving

Reference 17

Resolution: unresolved; no resolver link, observed 2026-06-27T19:28:52.968579Z

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Observation c9f0420c-2835-4751-820e-f25de2d423b5 · outbound

This paper cites A potential field-based model predictive path-planning con- troller for autonomous road vehicles.IEEE Transactions on Intelligent Transportation Systems, 18(5):1255–1267, 2016.

Learning Predictive Control with Deep Koopman Operators for Autonomous Vehicle Motion Planning A potential field-based model predictive path-planning con- troller for autonomous road vehicles.IEEE Transactions on Intelligent Transportation Systems, 18(5):1255–1267, 2016

Reference 18

Resolution: unresolved; no resolver link, observed 2026-06-27T19:28:52.968579Z

Source-reported events for the cited work

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Observation fb2caf79-509e-433e-9d75-aba10988cea3 · outbound

This paper cites Lateral vehicle trajectory optimization using constrained linear time-varying MPC.

Learning Predictive Control with Deep Koopman Operators for Autonomous Vehicle Motion Planning Lateral vehicle trajectory optimization using constrained linear time-varying MPC

Reference 19

Resolution: unresolved; no resolver link, observed 2026-06-27T19:28:52.968579Z

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Observation 766fb6e7-a3b9-4ac4-9ddc-527a445e6412 · outbound

This paper cites Path planning and tracking for vehicle collision avoidance based on model predictive control with multiconstraints.IEEE Transactions on Vehicular Technology, 66(2):952–964, 2016.

Learning Predictive Control with Deep Koopman Operators for Autonomous Vehicle Motion Planning Path planning and tracking for vehicle collision avoidance based on model predictive control with multiconstraints.IEEE Transactions on Vehicular Technology, 66(2):952–964, 2016

Reference 20

Resolution: unresolved; no resolver link, observed 2026-06-27T19:28:52.968579Z

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Observation f9f762d4-0738-4a5b-82b9-0826495c998a · outbound

This paper cites an unresolved cited work.

Learning Predictive Control with Deep Koopman Operators for Autonomous Vehicle Motion Planning Unresolved cited work

Reference 21

Resolution: unresolved; no resolver link, observed 2026-06-27T19:28:52.968579Z

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Observation bcf3f7a7-7b6c-461d-bf3a-20fa45b9d5cf · outbound

This paper cites Witherden, and Mykel J.

Learning Predictive Control with Deep Koopman Operators for Autonomous Vehicle Motion Planning Witherden, and Mykel J

Reference 22

Resolution: unresolved; no resolver link, observed 2026-06-27T19:28:52.968579Z

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Observation 3457b2ee-f9bf-45bd-ace3-ba243628c662 · outbound

This paper cites Joglekar et al.

Learning Predictive Control with Deep Koopman Operators for Autonomous Vehicle Motion Planning Joglekar et al

Reference 23

Resolution: unresolved; no resolver link, observed 2026-06-27T19:28:52.968579Z

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Observation bc5be460-04b4-4e45-a392-c97f02e56cc9 · outbound

This paper cites Deep koopman traffic modeling for freeway ramp metering.IEEE Transactions on Intelligent Transportation Systems, 24(6):6001–6013, 2023.

Learning Predictive Control with Deep Koopman Operators for Autonomous Vehicle Motion Planning Deep koopman traffic modeling for freeway ramp metering.IEEE Transactions on Intelligent Transportation Systems, 24(6):6001–6013, 2023

Reference 24

Resolution: unresolved; no resolver link, observed 2026-06-27T19:28:52.968579Z

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Observation 24d3f526-4d18-40d8-b4f7-2f9cefdb5f76 · outbound

This paper cites Transformers for modeling physical systems.Neural Networks, 146:272–289, 2022.

Learning Predictive Control with Deep Koopman Operators for Autonomous Vehicle Motion Planning Transformers for modeling physical systems.Neural Networks, 146:272–289, 2022

Reference 25

Resolution: unresolved; no resolver link, observed 2026-06-27T19:28:52.968579Z

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Observation 7427ce7a-6183-40a6-b74d-b91b2b6e9de5 · outbound

This paper cites Characterization of groundwater contamination: A transformer-based deep learning model.Advances in Water Resources, 164:104217, 2022.

Learning Predictive Control with Deep Koopman Operators for Autonomous Vehicle Motion Planning Characterization of groundwater contamination: A transformer-based deep learning model.Advances in Water Resources, 164:104217, 2022

Reference 26

Resolution: unresolved; no resolver link, observed 2026-06-27T19:28:52.968579Z

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Observation 38c03dc1-0f88-40cd-8742-f342fc847a0f · outbound

This paper cites Deepkoco: Efficient latent planning with a task-relevant koopman rep- resentation.

Learning Predictive Control with Deep Koopman Operators for Autonomous Vehicle Motion Planning Deepkoco: Efficient latent planning with a task-relevant koopman rep- resentation

Reference 27

Resolution: unresolved; no resolver link, observed 2026-06-27T19:28:52.968579Z

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Observation 71ad4991-822c-485d-a01f-922082c76e51 · outbound

This paper cites Near- optimal rapid MPC using neural networks: A primal-dual policy learn- ing framework.IEEE Transactions on Control Systems Technology, 29(5):2102–2114, 2020.

Learning Predictive Control with Deep Koopman Operators for Autonomous Vehicle Motion Planning Near- optimal rapid MPC using neural networks: A primal-dual policy learn- ing framework.IEEE Transactions on Control Systems Technology, 29(5):2102–2114, 2020

Reference 28

Resolution: unresolved; no resolver link, observed 2026-06-27T19:28:52.968579Z

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Observation ccf147f1-e5e0-452d-9535-eb36757f7249 · outbound

This paper cites Autonomous driving using linear model predictive control with a Koopman operator based bilinear vehicle model.IFAC-PapersOnLine, 55(24):254–259, 2022.

Learning Predictive Control with Deep Koopman Operators for Autonomous Vehicle Motion Planning Autonomous driving using linear model predictive control with a Koopman operator based bilinear vehicle model.IFAC-PapersOnLine, 55(24):254–259, 2022

Reference 29

Resolution: unresolved; no resolver link, observed 2026-06-27T19:28:52.968579Z

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Observation 6882aae4-3dc5-4bf4-8c37-903f84ba42a0 · outbound

This paper cites A review of end-to-end autonomous driving in urban environments.IEEE Access, 10:75296–75311, 2022.

Learning Predictive Control with Deep Koopman Operators for Autonomous Vehicle Motion Planning A review of end-to-end autonomous driving in urban environments.IEEE Access, 10:75296–75311, 2022

Reference 30

Resolution: unresolved; no resolver link, observed 2026-06-27T19:28:52.968579Z

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Observation 1b41a930-5c17-44b6-bdbe-18ff309e6b80 · outbound

This paper cites Deep reinforcement learning for autonomous driving: A survey.IEEE Transactions on Intelligent Transportation Systems, 23(6):4909–4926, 2021.

Learning Predictive Control with Deep Koopman Operators for Autonomous Vehicle Motion Planning Deep reinforcement learning for autonomous driving: A survey.IEEE Transactions on Intelligent Transportation Systems, 23(6):4909–4926, 2021

Reference 31

Resolution: unresolved; no resolver link, observed 2026-06-27T19:28:52.968579Z

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Observation cce3f128-c2f6-4026-bdfd-f4f13e891ca6 · outbound

This paper cites Real- time drift-driving control for an autonomous vehicle: Learning from nonlinear model predictive control via a deep neural network.Electron- ics, 11(17):2651, 2022.

Learning Predictive Control with Deep Koopman Operators for Autonomous Vehicle Motion Planning Real- time drift-driving control for an autonomous vehicle: Learning from nonlinear model predictive control via a deep neural network.Electron- ics, 11(17):2651, 2022

Reference 32

Resolution: unresolved; no resolver link, observed 2026-06-27T19:28:52.968579Z

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Observation 56c363a8-887b-4044-86f8-64fb5d39b14b · outbound

This paper cites Accept synthetic objects as real: End-to-end training of attentive deep visuomotor policies for manipulation in clutter.

Learning Predictive Control with Deep Koopman Operators for Autonomous Vehicle Motion Planning Accept synthetic objects as real: End-to-end training of attentive deep visuomotor policies for manipulation in clutter

Reference 33

Resolution: unresolved; no resolver link, observed 2026-06-27T19:28:52.968579Z

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Observation 42bad6fc-cb4e-4e8e-84b5-72d135183e67 · outbound

This paper cites End-to-end steering controller with cnn-based closed-loop feedback for autonomous vehicles.

Learning Predictive Control with Deep Koopman Operators for Autonomous Vehicle Motion Planning End-to-end steering controller with cnn-based closed-loop feedback for autonomous vehicles

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Resolution: unresolved; no resolver link, observed 2026-06-27T19:28:52.968579Z

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Unavailable: canonical work link unavailable.

Observation 8fb4b4f7-4ec7-4095-8ae3-a1d6b3990a10 · outbound

This paper cites Mixgail: Autonomous driving using demonstrations with mixed qualities.

Learning Predictive Control with Deep Koopman Operators for Autonomous Vehicle Motion Planning Mixgail: Autonomous driving using demonstrations with mixed qualities

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Observation 10ed96f6-ae0c-4ada-ae47-3e128081421c · outbound

This paper cites Drive Like a Human: Rethinking Autonomous Driving with Large Language Models.

Learning Predictive Control with Deep Koopman Operators for Autonomous Vehicle Motion Planning Drive Like a Human: Rethinking Autonomous Driving with Large Language Models

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Resolution: verified exact; arxiv_id, observed 2026-07-02T21:47:28.144121Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 9dcc33ef-f17a-4e74-88c8-4a1cf36c87d8 · outbound

This paper cites Model-free deep reinforcement learning for urban autonomous driving.

Learning Predictive Control with Deep Koopman Operators for Autonomous Vehicle Motion Planning Model-free deep reinforcement learning for urban autonomous driving

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Resolution: unresolved; no resolver link, observed 2026-06-27T19:28:52.968579Z

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Unavailable: canonical work link unavailable.

Observation 867bd0d1-e019-4023-9d1b-a224b47653e6 · outbound

This paper cites Uncertainty-aware model-based reinforcement learning: Methodology and application in autonomous driving.IEEE Transactions on Intelligent Vehicles, 8(1):194–203, 2022.

Learning Predictive Control with Deep Koopman Operators for Autonomous Vehicle Motion Planning Uncertainty-aware model-based reinforcement learning: Methodology and application in autonomous driving.IEEE Transactions on Intelligent Vehicles, 8(1):194–203, 2022

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Resolution: unresolved; no resolver link, observed 2026-06-27T19:28:52.968579Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dd768b8a-040c-4d10-91a5-44a444760302 · outbound

This paper cites Interpretable end-to-end urban autonomous driving with latent deep reinforcement learning.IEEE Transactions on Intelligent Transportation Systems, 2021.

Learning Predictive Control with Deep Koopman Operators for Autonomous Vehicle Motion Planning Interpretable end-to-end urban autonomous driving with latent deep reinforcement learning.IEEE Transactions on Intelligent Transportation Systems, 2021

Reference 40

Resolution: unresolved; no resolver link, observed 2026-06-27T19:28:52.968579Z

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Observation 34884efa-2d26-4c39-9023-fc96902c111a · outbound

This paper cites Self-learning cruise control using kernel-based least squares policy iter- ation.IEEE Transactions on Control Systems Technology, 22(3):1078– 1087, 2013.

Learning Predictive Control with Deep Koopman Operators for Autonomous Vehicle Motion Planning Self-learning cruise control using kernel-based least squares policy iter- ation.IEEE Transactions on Control Systems Technology, 22(3):1078– 1087, 2013

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Resolution: unresolved; no resolver link, observed 2026-06-27T19:28:52.968579Z

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Observation edd4be85-e6c2-4d88-9484-4a8ef2e1d7d8 · outbound

This paper cites An approxi- mate dynamic programming approach for path following control of an autonomous vehicle.

Learning Predictive Control with Deep Koopman Operators for Autonomous Vehicle Motion Planning An approxi- mate dynamic programming approach for path following control of an autonomous vehicle

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Resolution: unresolved; no resolver link, observed 2026-06-27T19:28:52.968579Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 82ce71fe-b751-4b7c-b2ca-f0b09e4033a2 · outbound

This paper cites Parameterized batch reinforcement learning for longitudinal control of autonomous land vehicles.IEEE Transactions on Systems, Man, and Cybernetics: Systems, 49(4):730–741, 2019.

Learning Predictive Control with Deep Koopman Operators for Autonomous Vehicle Motion Planning Parameterized batch reinforcement learning for longitudinal control of autonomous land vehicles.IEEE Transactions on Systems, Man, and Cybernetics: Systems, 49(4):730–741, 2019

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Resolution: unresolved; no resolver link, observed 2026-06-27T19:28:52.968579Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b355105f-8bed-497d-b157-7699084f9ac3 · outbound

This paper cites Learning-based predictive control for discrete-time nonlinear systems with stochastic disturbances.IEEE Transactions on Neural Networks and Learning Systems, 29(12):6202–6213, 2018.

Learning Predictive Control with Deep Koopman Operators for Autonomous Vehicle Motion Planning Learning-based predictive control for discrete-time nonlinear systems with stochastic disturbances.IEEE Transactions on Neural Networks and Learning Systems, 29(12):6202–6213, 2018

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Resolution: unresolved; no resolver link, observed 2026-06-27T19:28:52.968579Z

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Observation 8d7c59bf-32b9-4240-80ee-19230ea160f6 · outbound

This paper cites Deep neural networks with Koopman operators for modeling and control of autonomous vehicles.IEEE Transactions on Intelligent Vehicles, 8(1):135–146, 2023.

Learning Predictive Control with Deep Koopman Operators for Autonomous Vehicle Motion Planning Deep neural networks with Koopman operators for modeling and control of autonomous vehicles.IEEE Transactions on Intelligent Vehicles, 8(1):135–146, 2023

Reference 45

Resolution: unresolved; no resolver link, observed 2026-06-27T19:28:52.968579Z

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Observation 2ba924a7-196c-447c-bdc2-0e7158ab55f1 · outbound

This paper cites Model-based safe reinforcement learning with time-varying constraints: Applications to intelligent vehicles.IEEE Transactions on Industrial Electronics, 71(10):12744–12753, 2024.

Learning Predictive Control with Deep Koopman Operators for Autonomous Vehicle Motion Planning Model-based safe reinforcement learning with time-varying constraints: Applications to intelligent vehicles.IEEE Transactions on Industrial Electronics, 71(10):12744–12753, 2024

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Resolution: unresolved; no resolver link, observed 2026-06-27T19:28:52.968579Z

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Observation ad0f4361-4713-4e80-9d0f-a785e530945d · outbound

This paper cites Toward scalable multirobot control: Fast policy learning in distributed mpc.IEEE Transactions on Robotics, 41:1491– 1512, 2025.

Learning Predictive Control with Deep Koopman Operators for Autonomous Vehicle Motion Planning Toward scalable multirobot control: Fast policy learning in distributed mpc.IEEE Transactions on Robotics, 41:1491– 1512, 2025

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Resolution: unresolved; no resolver link, observed 2026-06-27T19:28:52.968579Z

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Pith citing papers

Observation e1f01248-5f31-43c3-b5ed-77dd905ba88f · inbound

Koopman Dreamer: Spectrally Constrained Latent Dynamics for Stable World-Model Imagination cites this paper.

Koopman Dreamer: Spectrally Constrained Latent Dynamics for Stable World-Model Imagination Learning Predictive Control with Deep Koopman Operators for Autonomous Vehicle Motion Planning

Reference 35

Resolution: verified exact; local_arxiv, observed 2026-08-01T12:03:35.570075Z

Source-reported events for the cited work

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Observation 4923fefa-db1b-4e6b-bb73-b59282012e72 · inbound

Koopman Dreamer: Spectrally Constrained Latent Dynamics for Stable World-Model Imagination cites this paper.

Koopman Dreamer: Spectrally Constrained Latent Dynamics for Stable World-Model Imagination Learning Predictive Control with Deep Koopman Operators for Autonomous Vehicle Motion Planning

Reference 33

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