From Noise to Control: Parameterized Diffusion Policies | Paper Citation Record

Observation ebfa7e27-6730-4141-888f-3dfbb0820abb · outbound

This paper cites Training diffusion models with reinforcement learning.

From Noise to Control: Parameterized Diffusion Policies Training diffusion models with reinforcement learning

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Observation acd3669b-2d31-4efd-81b0-f8c39ac3f291 · outbound

This paper cites On learning, representing, and generalizing a task in a humanoid robot.

From Noise to Control: Parameterized Diffusion Policies On learning, representing, and generalizing a task in a humanoid robot

Reference 2

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Observation 5523ba04-8e2e-4ae7-a513-9b0e0e17e6f6 · outbound

This paper cites Playfusion: Skill acquisition via diffusion from language-annotated play.

From Noise to Control: Parameterized Diffusion Policies Playfusion: Skill acquisition via diffusion from language-annotated play

Reference 3

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This paper cites Diffusion policy: Visuomotor policy learning via action diffusion.

From Noise to Control: Parameterized Diffusion Policies Diffusion policy: Visuomotor policy learning via action diffusion

Reference 4

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This paper cites Learning Parameterized Skills.

From Noise to Control: Parameterized Diffusion Policies Learning Parameterized Skills

Reference 5

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Observation 8a1e4a56-d158-4015-aa69-663a12e3d11c · outbound

This paper cites Accelerating robotic reinforcement learning via parameterized action primitives.

From Noise to Control: Parameterized Diffusion Policies Accelerating robotic reinforcement learning via parameterized action primitives

Reference 6

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This paper cites and Nichol, A.

From Noise to Control: Parameterized Diffusion Policies and Nichol, A

Reference 7

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This paper cites Diffusion-based reinforcement learning via q-weighted variational policy optimization.

From Noise to Control: Parameterized Diffusion Policies Diffusion-based reinforcement learning via q-weighted variational policy optimization

Reference 8

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Observation e7cd3f83-d169-4b8a-9064-96355615e012 · outbound

This paper cites Genpo: Generative diffusion models meet on-policy reinforcement learning.arXiv preprint arXiv:2505.18763.

From Noise to Control: Parameterized Diffusion Policies Genpo: Generative diffusion models meet on-policy reinforcement learning.arXiv preprint arXiv:2505.18763

Reference 9

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This paper cites and Mordatch, I.

From Noise to Control: Parameterized Diffusion Policies and Mordatch, I

Reference 10

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This paper cites B., Dieleman, S., Fergus, R., Sohl-Dickstein, J., Doucet, A., and Grathwohl, W.

From Noise to Control: Parameterized Diffusion Policies B., Dieleman, S., Fergus, R., Sohl-Dickstein, J., Doucet, A., and Grathwohl, W

Reference 11

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This paper cites Learning universal policies via text-guided video generation.

From Noise to Control: Parameterized Diffusion Policies Learning universal policies via text-guided video generation

Reference 12

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This paper cites A., Wahid, A., Downs, L., Adrianos, A., Hsu, C.-Y., and Chi, C.

From Noise to Control: Parameterized Diffusion Policies A., Wahid, A., Downs, L., Adrianos, A., Hsu, C.-Y., and Chi, C

Reference 13

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Observation 9972518a-6607-4f84-b697-ebea4deddd69 · outbound

This paper cites Meta learning shared hierarchies.

From Noise to Control: Parameterized Diffusion Policies Meta learning shared hierarchies

Reference 14

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Observation cbcac1b9-ab56-4b8b-92c6-3cb385f3f178 · outbound

This paper cites Meta-learning parameterized skills.

From Noise to Control: Parameterized Diffusion Policies Meta-learning parameterized skills

Reference 15

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Observation ac966eea-419c-4835-8c78-099f5f734455 · outbound

This paper cites Relay policy learning: Solving long-horizon tasks via imitation and reinforcement learning.

From Noise to Control: Parameterized Diffusion Policies Relay policy learning: Solving long-horizon tasks via imitation and reinforcement learning

Reference 16

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This paper cites Learning parameterized skills from demonstrations.

From Noise to Control: Parameterized Diffusion Policies Learning parameterized skills from demonstrations

Reference 17

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This paper cites Isometric representation learning for disentangled latent space of diffusion models.

From Noise to Control: Parameterized Diffusion Policies Isometric representation learning for disentangled latent space of diffusion models

Reference 18

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From Noise to Control: Parameterized Diffusion Policies Unresolved cited work

Reference 19

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Observation be5101ad-c14a-45a8-a39f-e55cf07b6811 · outbound

This paper cites Denoising diffusion probabilistic models.

From Noise to Control: Parameterized Diffusion Policies Denoising diffusion probabilistic models

Reference 20

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This paper cites In: 2025 IEEE International Conference on Robotics and Automation (ICRA), pp.

From Noise to Control: Parameterized Diffusion Policies In: 2025 IEEE International Conference on Robotics and Automation (ICRA), pp

Reference 21

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Observation 1f6e0e95-ca81-4188-a278-d9ded12e17e2 · outbound

This paper cites Multimodal deep generative models for trajectory prediction: A conditional variational autoencoder approach.

From Noise to Control: Parameterized Diffusion Policies Multimodal deep generative models for trajectory prediction: A conditional variational autoencoder approach

Reference 22

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This paper cites Policy-Guided Diffusion.

From Noise to Control: Parameterized Diffusion Policies Policy-Guided Diffusion

Reference 23

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This paper cites B., and Levine, S.

From Noise to Control: Parameterized Diffusion Policies B., and Levine, S

Reference 24

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This paper cites Towards diverse behaviors: A benchmark for imitation learning with human demonstrations.

From Noise to Control: Parameterized Diffusion Policies Towards diverse behaviors: A benchmark for imitation learning with human demonstrations

Reference 25

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This paper cites Efficient diffusion policies for offline reinforcement learning.

From Noise to Control: Parameterized Diffusion Policies Efficient diffusion policies for offline reinforcement learning

Reference 26

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This paper cites Elucidating the design space of diffusion-based generative models.

From Noise to Control: Parameterized Diffusion Policies Elucidating the design space of diffusion-based generative models

Reference 27

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From Noise to Control: Parameterized Diffusion Policies Unresolved cited work

Reference 28

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This paper cites J., Shafiullah, N.

From Noise to Control: Parameterized Diffusion Policies J., Shafiullah, N

Reference 29

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This paper cites arXiv preprint arXiv:2506.03067 (2025).

From Noise to Control: Parameterized Diffusion Policies arXiv preprint arXiv:2506.03067 (2025)

Reference 30

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This paper cites C., Zhai, J., and Ma, S.

From Noise to Control: Parameterized Diffusion Policies C., Zhai, J., and Ma, S

Reference 31

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This paper cites Learning multimodal behaviors from scratch with diffusion policy gradient.

From Noise to Control: Parameterized Diffusion Policies Learning multimodal behaviors from scratch with diffusion policy gradient

Reference 32

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This paper cites Learning multimodal behaviors from scratch with diffusion policy gradient.

From Noise to Control: Parameterized Diffusion Policies Learning multimodal behaviors from scratch with diffusion policy gradient

Reference 33

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This paper cites Adpro: a test-time adaptive diffusion policy via manifold-constrained denoising and task-aware initialization for robotic manipulation.

From Noise to Control: Parameterized Diffusion Policies Adpro: a test-time adaptive diffusion policy via manifold-constrained denoising and task-aware initialization for robotic manipulation

Reference 34

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From Noise to Control: Parameterized Diffusion Policies Learning latent plans from play

Reference 35

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This paper cites Reinforcement Learning with Discrete Diffusion Policies for Combinatorial Action Spaces.

From Noise to Control: Parameterized Diffusion Policies Reinforcement Learning with Discrete Diffusion Policies for Combinatorial Action Spaces

Reference 36

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This paper cites Diffusionrl: Efficient training of diffusion policies for robotic grasping using rl-adapted large- scale datasets.

From Noise to Control: Parameterized Diffusion Policies Diffusionrl: Efficient training of diffusion policies for robotic grasping using rl-adapted large- scale datasets

Reference 37

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This paper cites What matters in learning from offline demonstrations for robot manipulation.

From Noise to Control: Parameterized Diffusion Policies What matters in learning from offline demonstrations for robot manipulation

Reference 38

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From Noise to Control: Parameterized Diffusion Policies Unresolved cited work

Reference 39

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This paper cites Emogen: Emotional image content generation with text-to-image diffusion models.

From Noise to Control: Parameterized Diffusion Policies Emogen: Emotional image content generation with text-to-image diffusion models

Reference 40

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This paper cites B., Shanbhag, A.

From Noise to Control: Parameterized Diffusion Policies B., Shanbhag, A

Reference 41

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This paper cites and Dhariwal, P.

From Noise to Control: Parameterized Diffusion Policies and Dhariwal, P

Reference 42

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This paper cites Much ado about noising: Dispelling the myths of gener- ative robotic control.

From Noise to Control: Parameterized Diffusion Policies Much ado about noising: Dispelling the myths of gener- ative robotic control

Reference 43

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This paper cites Semantics lead the way: Harmonizing semantic and texture modeling with asynchronous latent diffusion.

From Noise to Control: Parameterized Diffusion Policies Semantics lead the way: Harmonizing semantic and texture modeling with asynchronous latent diffusion

Reference 44

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This paper cites Whistler waves generated inside magnetic dips in the young solar wind: observations of the Search-Coil Magnetometer on board Parker Solar Probe.

From Noise to Control: Parameterized Diffusion Policies Whistler waves generated inside magnetic dips in the young solar wind: observations of the Search-Coil Magnetometer on board Parker Solar Probe

Reference 45

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This paper cites Film: Visual reasoning with a general conditioning layer.

From Noise to Control: Parameterized Diffusion Policies Film: Visual reasoning with a general conditioning layer

Reference 46

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This paper cites Offline Reinforcement Learning with Discrete Diffusion Skills.

From Noise to Control: Parameterized Diffusion Policies Offline Reinforcement Learning with Discrete Diffusion Skills

Reference 47

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From Noise to Control: Parameterized Diffusion Policies what it’s like

Reference 48

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This paper cites Goal-conditioned imitation learning using score-based diffusion policies.

From Noise to Control: Parameterized Diffusion Policies Goal-conditioned imitation learning using score-based diffusion policies

Reference 49

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From Noise to Control: Parameterized Diffusion Policies Forward kl regularized preference optimization for aligning diffusion policies

Reference 50

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From Noise to Control: Parameterized Diffusion Policies A., Maheswaranathan, N., and Ganguli, S

Reference 51

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From Noise to Control: Parameterized Diffusion Policies and Ermon, S

Reference 52

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From Noise to Control: Parameterized Diffusion Policies P., Kumar, A., Ermon, S., and Poole, B

Reference 53

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From Noise to Control: Parameterized Diffusion Policies S., Precup, D., and Singh, S

Reference 54

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From Noise to Control: Parameterized Diffusion Policies S., Osindero, S., Schaul, T., Heess, N., Jaderberg, M., Silver, D., and Kavukcuoglu, K

Reference 55

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This paper cites Steering your diffusion policy with latent space reinforcement learning.

From Noise to Control: Parameterized Diffusion Policies Steering your diffusion policy with latent space reinforcement learning

Reference 56

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This paper cites J., and Zhou, M.

From Noise to Control: Parameterized Diffusion Policies J., and Zhou, M

Reference 57

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This paper cites Learning intractable multimodal policies with reparameterization and diversity regularization.

From Noise to Control: Parameterized Diffusion Policies Learning intractable multimodal policies with reparameterization and diversity regularization

Reference 58

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Observation 6592bb4a-d460-46f8-9e00-bbed3a62f482 · outbound

This paper cites Diffusion models for robotic manipulation: A survey.

From Noise to Control: Parameterized Diffusion Policies Diffusion models for robotic manipulation: A survey

Reference 59

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Observation d2399360-1f24-4721-a22c-f62fb822861b · outbound

This paper cites In: 2025 IEEE International Conference on Robotics and Automation (ICRA), pp.

From Noise to Control: Parameterized Diffusion Policies In: 2025 IEEE International Conference on Robotics and Automation (ICRA), pp

Reference 60

Resolution: metadata mismatch; arxiv_id, observed 2026-06-28T22:12:41.446023Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-31T06:34:12.847434+00:00.

Observation dd38142f-f180-44df-9092-d0d10f72071d · outbound

This paper cites Diffusion models for reinforcement learning: Foundations, taxonomy, and development.

From Noise to Control: Parameterized Diffusion Policies Diffusion models for reinforcement learning: Foundations, taxonomy, and development

Reference 61

Resolution: verified exact; arxiv_id, observed 2026-07-01T19:46:10.565037Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-31T06:34:12.847434+00:00.

Observation 82af1bac-4229-48f9-8c2a-6325bc3d03fc · outbound

This paper cites Diffusion- ES : Gradient-free planning with diffusion for autonomous driving and zero-shot instruction following.

From Noise to Control: Parameterized Diffusion Policies Diffusion- ES : Gradient-free planning with diffusion for autonomous driving and zero-shot instruction following

Reference 62

Resolution: unresolved; no resolver link, observed 2026-06-28T22:06:53.667826Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 50d123b8-00ae-4711-875e-bc8c60158445 · outbound

This paper cites Efficient task-specific conditional diffusion policies: Shortcut model acceleration and so (3) optimization.

From Noise to Control: Parameterized Diffusion Policies Efficient task-specific conditional diffusion policies: Shortcut model acceleration and so (3) optimization

Reference 63

Resolution: unresolved; no resolver link, observed 2026-06-28T22:06:53.667826Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 946fe76e-a1da-44b9-ba5a-31cf365f8927 · outbound

This paper cites Model-based reinforcement learning for parameterized action spaces.

From Noise to Control: Parameterized Diffusion Policies Model-based reinforcement learning for parameterized action spaces

Reference 64

Resolution: unresolved; no resolver link, observed 2026-06-28T22:06:53.667826Z

Source-reported events for the cited work

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Observation 7978ef9f-0840-4746-9d55-876dc366515e · outbound

This paper cites D., Huang, F., and Kolobov, A.

From Noise to Control: Parameterized Diffusion Policies D., Huang, F., and Kolobov, A

Reference 65

Resolution: unresolved; no resolver link, observed 2026-06-28T22:06:53.667826Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation abd60a2b-3ffb-495c-9573-6798dc212d05 · outbound

This paper cites N., and Gao, R.

From Noise to Control: Parameterized Diffusion Policies N., and Gao, R

Reference 66

Resolution: unresolved; no resolver link, observed 2026-06-28T22:06:53.667826Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f084e249-8118-4c72-9fb9-e11ca282f95a · outbound

This paper cites Diffusion Models for Reinforcement Learning: A Survey.

From Noise to Control: Parameterized Diffusion Policies Diffusion Models for Reinforcement Learning: A Survey

Reference 67

Resolution: metadata mismatch; arxiv_id, observed 2026-07-01T19:46:10.592151Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-31T06:34:12.847434+00:00.

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