Energy-Structured Latent World Models with Neural Time Fields for Physically Constistent Open-World Motion Planning | Paper Citation Record

Observation 7fe3c009-5c96-4f10-8906-85fd8dda5d75 · outbound

This paper cites Advances in neural information processing systems , volume=.

Energy-Structured Latent World Models with Neural Time Fields for Physically Constistent Open-World Motion Planning Advances in neural information processing systems , volume=

Reference 1

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This paper cites International conference on machine learning , pages=.

Energy-Structured Latent World Models with Neural Time Fields for Physically Constistent Open-World Motion Planning International conference on machine learning , pages=

Reference 2

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Observation 2f2855ef-c7cd-4118-8e3a-41398fb0be03 · outbound

This paper cites Dream to Control: Learning Behaviors by Latent Imagination.

Energy-Structured Latent World Models with Neural Time Fields for Physically Constistent Open-World Motion Planning Dream to Control: Learning Behaviors by Latent Imagination

Reference 3

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Observation fa88e5a1-a1f8-4459-a10c-80c87a46c50c · outbound

This paper cites Mastering Diverse Domains through World Models.

Energy-Structured Latent World Models with Neural Time Fields for Physically Constistent Open-World Motion Planning Mastering Diverse Domains through World Models

Reference 4

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This paper cites LeJEPA: Provable and Scalable Self-Supervised Learning Without the Heuristics.

Energy-Structured Latent World Models with Neural Time Fields for Physically Constistent Open-World Motion Planning LeJEPA: Provable and Scalable Self-Supervised Learning Without the Heuristics

Reference 5

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This paper cites International Conference on Machine Learning , pages=.

Energy-Structured Latent World Models with Neural Time Fields for Physically Constistent Open-World Motion Planning International Conference on Machine Learning , pages=

Reference 6

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Observation 7f35051c-3489-42ad-9138-a2b891d9e1c7 · outbound

This paper cites 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , pages=.

Energy-Structured Latent World Models with Neural Time Fields for Physically Constistent Open-World Motion Planning 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , pages=

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:27:52.511428Z

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Observation 77ed20f0-8307-442b-bbb9-b96143578a97 · outbound

This paper cites arXiv preprint arXiv:2606.15768 , year=.

Energy-Structured Latent World Models with Neural Time Fields for Physically Constistent Open-World Motion Planning arXiv preprint arXiv:2606.15768 , year=

Reference 8

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Observation d6d3aca9-f7ac-4847-b3f3-9d7db7732bb1 · outbound

This paper cites Advances in Neural Information Processing Systems , volume=.

Energy-Structured Latent World Models with Neural Time Fields for Physically Constistent Open-World Motion Planning Advances in Neural Information Processing Systems , volume=

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:27:52.500587Z

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Observation 6066543d-f33e-47eb-8144-e113fcd0cf9d · outbound

This paper cites Advances in neural information processing systems , volume=.

Energy-Structured Latent World Models with Neural Time Fields for Physically Constistent Open-World Motion Planning Advances in neural information processing systems , volume=

Reference 10

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Observation 9e2d0cd6-8758-4182-b672-f23a10cbd00a · outbound

This paper cites Lagrangian Neural Networks.

Energy-Structured Latent World Models with Neural Time Fields for Physically Constistent Open-World Motion Planning Lagrangian Neural Networks

Reference 11

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Observation c978c644-d380-4c87-91dc-87e4623d6203 · outbound

This paper cites Dissipative SymODEN: Encoding Hamiltonian Dynamics with Dissipation and Control into Deep Learning.

Energy-Structured Latent World Models with Neural Time Fields for Physically Constistent Open-World Motion Planning Dissipative SymODEN: Encoding Hamiltonian Dynamics with Dissipation and Control into Deep Learning

Reference 12

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Observation 4f6cf274-50c3-4fbf-8202-9258cef671e0 · outbound

This paper cites Port-Hamiltonian Neural Networks for Learning Explicit Time-Dependent Dynamical Systems.

Energy-Structured Latent World Models with Neural Time Fields for Physically Constistent Open-World Motion Planning Port-Hamiltonian Neural Networks for Learning Explicit Time-Dependent Dynamical Systems

Reference 13

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Observation f5cb148a-de69-451e-8331-9fd6ecef6b79 · outbound

This paper cites SkyJEPA: Learning Long-Horizon World Models for Zero-Shot Sim-to-Real Control of Quadrotors.

Energy-Structured Latent World Models with Neural Time Fields for Physically Constistent Open-World Motion Planning SkyJEPA: Learning Long-Horizon World Models for Zero-Shot Sim-to-Real Control of Quadrotors

Reference 14

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Observation 2e8745f7-f511-47c5-a6c1-baba525a0c0f · outbound

This paper cites Journal of Computational physics , volume=.

Energy-Structured Latent World Models with Neural Time Fields for Physically Constistent Open-World Motion Planning Journal of Computational physics , volume=

Reference 15

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This paper cites , author=.

Energy-Structured Latent World Models with Neural Time Fields for Physically Constistent Open-World Motion Planning , author=

Reference 16

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Observation 2b4b0c48-de9a-433a-8c49-840f8425a8e3 · outbound

This paper cites The international journal of robotics research , volume=.

Energy-Structured Latent World Models with Neural Time Fields for Physically Constistent Open-World Motion Planning The international journal of robotics research , volume=

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-15T14:27:51.490028Z

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Observation 7210a465-c968-4bea-ab42-eb9a61faaa1f · outbound

This paper cites IEEE Transactions on Robotics , volume=.

Energy-Structured Latent World Models with Neural Time Fields for Physically Constistent Open-World Motion Planning IEEE Transactions on Robotics , volume=

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:27:52.462520Z

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Observation 4886ecb7-adf6-4843-a4fe-3e79fec20936 · outbound

This paper cites IEEE Transactions on Geoscience and Remote Sensing , volume=.

Energy-Structured Latent World Models with Neural Time Fields for Physically Constistent Open-World Motion Planning IEEE Transactions on Geoscience and Remote Sensing , volume=

Reference 19

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Observation 517b357c-4dab-4067-8098-a3b9a9630aca · outbound

This paper cites Computers & Geosciences , volume=.

Energy-Structured Latent World Models with Neural Time Fields for Physically Constistent Open-World Motion Planning Computers & Geosciences , volume=

Reference 20

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Observation d4834340-8778-4b0e-ab4d-34ee0c7d54f7 · outbound

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

Energy-Structured Latent World Models with Neural Time Fields for Physically Constistent Open-World Motion Planning NTFields: Neural Time Fields for Physics-Informed Robot Motion Planning

Reference 21

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Observation ddf3a970-3340-4148-89d1-43af2575ea3d · outbound

This paper cites Robotics: Science and Systems , year =.

Energy-Structured Latent World Models with Neural Time Fields for Physically Constistent Open-World Motion Planning Robotics: Science and Systems , year =

Reference 22

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Observation 8b51d590-5286-400e-a373-3631cf6a2d75 · outbound

This paper cites IEEE Transactions on Robotics , year=.

Energy-Structured Latent World Models with Neural Time Fields for Physically Constistent Open-World Motion Planning IEEE Transactions on Robotics , year=

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:27:52.420090Z

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Observation 8e1af2d9-fdde-411a-b0cd-960b298d5252 · outbound

This paper cites Physics-Informed Eikonal Caging for Whole-Arm Manipulation Planning.

Energy-Structured Latent World Models with Neural Time Fields for Physically Constistent Open-World Motion Planning Physics-Informed Eikonal Caging for Whole-Arm Manipulation Planning

Reference 24

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Observation f6f6a56c-8c70-42ce-b865-2eda6e0e63f9 · outbound

This paper cites IEEE Robotics and Automation Letters , volume=.

Energy-Structured Latent World Models with Neural Time Fields for Physically Constistent Open-World Motion Planning IEEE Robotics and Automation Letters , volume=

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:27:52.409299Z

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Observation 35dd8f8c-30bc-436d-a88c-0539ec2f374c · outbound

This paper cites IEEE Robotics and Automation Letters , volume=.

Energy-Structured Latent World Models with Neural Time Fields for Physically Constistent Open-World Motion Planning IEEE Robotics and Automation Letters , volume=

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:27:52.397633Z

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Observation 5b1fcf93-db44-4163-9902-da5c9aff633a · outbound

This paper cites 2022 , doi =.

Energy-Structured Latent World Models with Neural Time Fields for Physically Constistent Open-World Motion Planning 2022 , doi =

Reference 27

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Observation 030adc8e-bc30-4566-bb39-b4fd302f0056 · outbound

This paper cites iGibson 1.0: a Simulation Environment for Interactive Tasks in Large Realistic Scenes.

Energy-Structured Latent World Models with Neural Time Fields for Physically Constistent Open-World Motion Planning iGibson 1.0: a Simulation Environment for Interactive Tasks in Large Realistic Scenes

Reference 28

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This paper cites Habitat-Matterport 3D Dataset (HM3D): 1000 Large-scale 3D Environments for Embodied AI.

Energy-Structured Latent World Models with Neural Time Fields for Physically Constistent Open-World Motion Planning Habitat-Matterport 3D Dataset (HM3D): 1000 Large-scale 3D Environments for Embodied AI

Reference 29

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This paper cites Recent Mathematical Methods in Dynamic Programming: Proceedings of the Conference held in Rome, Italy, March 26--28, 1984 , pages=.

Energy-Structured Latent World Models with Neural Time Fields for Physically Constistent Open-World Motion Planning Recent Mathematical Methods in Dynamic Programming: Proceedings of the Conference held in Rome, Italy, March 26--28, 1984 , pages=

Reference 30

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Observation ed5da39d-6811-4bd1-bec6-ecc73ed03f19 · outbound

This paper cites arXiv preprint arXiv:2512.10942 , year=.

Energy-Structured Latent World Models with Neural Time Fields for Physically Constistent Open-World Motion Planning arXiv preprint arXiv:2512.10942 , year=

Reference 31

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This paper cites arXiv preprint arXiv:2606.16076 , year=.

Energy-Structured Latent World Models with Neural Time Fields for Physically Constistent Open-World Motion Planning arXiv preprint arXiv:2606.16076 , year=

Reference 32

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Observation 81b51305-aa5d-4577-a9fe-19580bb69f9d · outbound

This paper cites Proceedings of the IEEE , volume=.

Energy-Structured Latent World Models with Neural Time Fields for Physically Constistent Open-World Motion Planning Proceedings of the IEEE , volume=

Reference 33

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Observation 18897452-fb25-4b94-83ba-2e40c48b5c45 · outbound

This paper cites PIN-WM: Learning Physics-INformed World Models for Non-Prehensile Manipulation.

Energy-Structured Latent World Models with Neural Time Fields for Physically Constistent Open-World Motion Planning PIN-WM: Learning Physics-INformed World Models for Non-Prehensile Manipulation

Reference 34

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This paper cites Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages=.

Energy-Structured Latent World Models with Neural Time Fields for Physically Constistent Open-World Motion Planning Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages=

Reference 35

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This paper cites Advances in Neural Information Processing Systems , volume=.

Energy-Structured Latent World Models with Neural Time Fields for Physically Constistent Open-World Motion Planning Advances in Neural Information Processing Systems , volume=

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:27:52.352069Z

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Observation dcd5d2e2-e6db-4e76-a069-70f674e1e49f · outbound

This paper cites IEEE Transactions on Robotics , volume=.

Energy-Structured Latent World Models with Neural Time Fields for Physically Constistent Open-World Motion Planning IEEE Transactions on Robotics , volume=

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:27:52.341570Z

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Observation b834b4af-c82b-419d-8780-e9a28226e5e8 · outbound

This paper cites 2, 2022-06-27 , author=.

Energy-Structured Latent World Models with Neural Time Fields for Physically Constistent Open-World Motion Planning 2, 2022-06-27 , author=

Reference 38

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This paper cites Aerospace Science and Technology , pages=.

Energy-Structured Latent World Models with Neural Time Fields for Physically Constistent Open-World Motion Planning Aerospace Science and Technology , pages=

Reference 39

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Observation a2947c98-e2be-4fed-b681-0667768815b7 · outbound

This paper cites Advances in Neural Information Processing Systems , volume=.

Energy-Structured Latent World Models with Neural Time Fields for Physically Constistent Open-World Motion Planning Advances in Neural Information Processing Systems , volume=

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:27:52.312598Z

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Observation 53077f85-d423-46a2-8a32-8d6cd7f6a332 · outbound

This paper cites PH-Dreamer: A Physics-Driven World Model via Port-Hamiltonian Generative Dynamics.

Energy-Structured Latent World Models with Neural Time Fields for Physically Constistent Open-World Motion Planning PH-Dreamer: A Physics-Driven World Model via Port-Hamiltonian Generative Dynamics

Reference 41

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Observation 76ea927d-a949-49d4-a0bd-5adb956369ee · outbound

This paper cites Symplectic ODE-Net: Learning Hamiltonian Dynamics with Control.

Energy-Structured Latent World Models with Neural Time Fields for Physically Constistent Open-World Motion Planning Symplectic ODE-Net: Learning Hamiltonian Dynamics with Control

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Observation fed09167-cb75-413e-b1ef-036b4681427e · outbound

This paper cites 7th Annual Learning for Dynamics & Control Conference, 04-06 June, 2025, Ann Arbor, Michigan, USA , pages=.

Energy-Structured Latent World Models with Neural Time Fields for Physically Constistent Open-World Motion Planning 7th Annual Learning for Dynamics & Control Conference, 04-06 June, 2025, Ann Arbor, Michigan, USA , pages=

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:27:52.301854Z

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Observation 22ee2c43-54db-42c1-b71f-07ef40547237 · outbound

This paper cites The International Journal of Robotics Research , volume=.

Energy-Structured Latent World Models with Neural Time Fields for Physically Constistent Open-World Motion Planning The International Journal of Robotics Research , volume=

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:27:52.290973Z

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Observation 7440691b-3d24-4da5-9bd1-69959659a9c0 · outbound

This paper cites IEEE/ASME Transactions on Mechatronics , year=.

Energy-Structured Latent World Models with Neural Time Fields for Physically Constistent Open-World Motion Planning IEEE/ASME Transactions on Mechatronics , year=

Reference 45

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Observation dabb03dd-a52d-4ae6-9869-8c5831457292 · outbound

This paper cites , author=.

Energy-Structured Latent World Models with Neural Time Fields for Physically Constistent Open-World Motion Planning , author=

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:27:52.273700Z

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Observation 31d28beb-22bd-4adb-98aa-9bd96ca31fce · outbound

This paper cites IEEE Transactions on Robotics , volume=.

Energy-Structured Latent World Models with Neural Time Fields for Physically Constistent Open-World Motion Planning IEEE Transactions on Robotics , volume=

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:27:52.261830Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 8d9ce185-cfce-4ce2-b00a-eb2029a42e55 · outbound

This paper cites arXiv preprint arXiv:2602.11291 , year=.

Energy-Structured Latent World Models with Neural Time Fields for Physically Constistent Open-World Motion Planning arXiv preprint arXiv:2602.11291 , year=

Reference 48

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Observation 2bf8b593-a4cb-4c0f-81d8-5462a19c425f · outbound

This paper cites Proceedings of the AAAI Conference on Artificial Intelligence , volume=.

Energy-Structured Latent World Models with Neural Time Fields for Physically Constistent Open-World Motion Planning Proceedings of the AAAI Conference on Artificial Intelligence , volume=

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:27:52.248623Z

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Observation 210f6cba-4bfc-4d07-9c1e-34b325d6d5ef · outbound

This paper cites LeWorldModel: Stable End-to-End Joint-Embedding Predictive Architecture from Pixels.

Energy-Structured Latent World Models with Neural Time Fields for Physically Constistent Open-World Motion Planning LeWorldModel: Stable End-to-End Joint-Embedding Predictive Architecture from Pixels

Reference 50

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Observation d0115dae-6f37-4342-a4b8-a67e89054a5f · outbound

This paper cites Deep Lagrangian Networks: Using Physics as Model Prior for Deep Learning.

Energy-Structured Latent World Models with Neural Time Fields for Physically Constistent Open-World Motion Planning Deep Lagrangian Networks: Using Physics as Model Prior for Deep Learning

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Observation bb3b3773-a531-4234-beaa-e349d49fae1d · outbound

This paper cites arXiv preprint arXiv:2607.03339 , year=.

Energy-Structured Latent World Models with Neural Time Fields for Physically Constistent Open-World Motion Planning arXiv preprint arXiv:2607.03339 , year=

Reference 52

Resolution: verified exact; raw_fallback, observed 2026-08-15T14:27:51.799483Z

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Observation 22f67bb5-9af8-4775-b43d-02775f8fd791 · outbound

This paper cites RMA: Rapid Motor Adaptation for Legged Robots.

Energy-Structured Latent World Models with Neural Time Fields for Physically Constistent Open-World Motion Planning RMA: Rapid Motor Adaptation for Legged Robots

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-15T14:27:51.618116Z

Source-reported events for the cited work

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Observation 2c8a8230-8428-4347-b18e-f986976863e4 · outbound

This paper cites World Action Models are Zero-shot Policies.

Energy-Structured Latent World Models with Neural Time Fields for Physically Constistent Open-World Motion Planning World Action Models are Zero-shot Policies

Reference 54

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Energy-Structured Latent World Models with Neural Time Fields for Physically Constistent Open-World Motion Planning 1997 , publisher=

Reference 55

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