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Paper Citation Record · LEDGER

Hierarchical Diffusion Policy: manipulation trajectory generation via contact guidance

As of 14 August 2026, this Paper Citation Record lists 70 of 70 outbound references and 0 inbound Pith citation observations for arXiv:2411.12982.

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

pith.paper-citation-record.v1
2411.12982 v1

Coverage vector

measured 70 of 70 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T17:06:32.495395Z

measured 70 of 70 standing notices

One-hop event checks from named stored sources.

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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

70 of 70 outbound references displayed

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  • unresolved27
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation cfda12c4-2276-4e75-8f10-3568915b9eb5 · outbound

This paper cites Human-level control through deep reinforcement learning,.

Hierarchical Diffusion Policy: manipulation trajectory generation via contact guidance Human-level control through deep reinforcement learning,

Reference 1

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Observation 1394b0a7-4b62-4b30-8a67-ed27074c6232 · outbound

This paper cites Exploration in deep rein- forcement learning: A survey,.

Hierarchical Diffusion Policy: manipulation trajectory generation via contact guidance Exploration in deep rein- forcement learning: A survey,

Reference 2

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Observation 044d80b8-9dda-41c2-a324-f3087513582b · outbound

This paper cites Imitation learning: Progress, taxonomies and challenges,.

Hierarchical Diffusion Policy: manipulation trajectory generation via contact guidance Imitation learning: Progress, taxonomies and challenges,

Reference 3

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Observation cadc9461-5a1c-41c0-95b3-a42b51a496e9 · outbound

This paper cites Denoising Diffusion Implicit Models.

Hierarchical Diffusion Policy: manipulation trajectory generation via contact guidance Denoising Diffusion Implicit Models

Reference 4

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Observation 1712c351-044f-4290-b0f1-8056f7a02fe1 · outbound

This paper cites Improved denoising diffusion proba- bilistic models,.

Hierarchical Diffusion Policy: manipulation trajectory generation via contact guidance Improved denoising diffusion proba- bilistic models,

Reference 5

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Observation 90e6cb75-bebe-4feb-98e1-332bfcb9129b · outbound

This paper cites High- resolution image synthesis with latent diffusion models,.

Hierarchical Diffusion Policy: manipulation trajectory generation via contact guidance High- resolution image synthesis with latent diffusion models,

Reference 6

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Observation 01534b9c-6941-4035-a1cf-bde1cf9e43ba · outbound

This paper cites Diffusion Policies as an Expressive Policy Class for Offline Reinforcement Learning.

Hierarchical Diffusion Policy: manipulation trajectory generation via contact guidance Diffusion Policies as an Expressive Policy Class for Offline Reinforcement Learning

Reference 7

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Observation 6d7eccd7-cf17-4fdb-b994-5ce2f650d70b · outbound

This paper cites Planning with diffusion for flexible behavior synthesis,.

Hierarchical Diffusion Policy: manipulation trajectory generation via contact guidance Planning with diffusion for flexible behavior synthesis,

Reference 8

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation 0547843b-587a-4aa8-a99e-4cbfd929dfab · outbound

This paper cites Diffusion Policy: Visuomotor Policy Learning via Action Diffusion.

Hierarchical Diffusion Policy: manipulation trajectory generation via contact guidance Diffusion Policy: Visuomotor Policy Learning via Action Diffusion

Reference 9

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Observation 999c0232-4fa7-484d-ab07-36d20f2deae3 · outbound

This paper cites Recent trends in task and motion planning for robotics: A survey,.

Hierarchical Diffusion Policy: manipulation trajectory generation via contact guidance Recent trends in task and motion planning for robotics: A survey,

Reference 10

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Observation 0ded3126-a8f4-4566-a1d4-66c393f2514f · outbound

This paper cites Sampling-based motion planning: A comparative review,.

Hierarchical Diffusion Policy: manipulation trajectory generation via contact guidance Sampling-based motion planning: A comparative review,

Reference 11

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Observation 43399ff3-c472-4c5a-baaa-07bae90f1001 · outbound

This paper cites Hybrid hierarchical learn- ing for solving complex sequential tasks using the robotic manipulation network roman,.

Hierarchical Diffusion Policy: manipulation trajectory generation via contact guidance Hybrid hierarchical learn- ing for solving complex sequential tasks using the robotic manipulation network roman,

Reference 12

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation 6adf61de-5354-4af2-bd96-561b7bcecf61 · outbound

This paper cites Reinforcement learning in robotic applications: a comprehensive survey,.

Hierarchical Diffusion Policy: manipulation trajectory generation via contact guidance Reinforcement learning in robotic applications: a comprehensive survey,

Reference 13

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Source-reported events for the cited work

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Observation 9bbaeea3-c9a6-4b16-a447-0e6bb867a6ec · outbound

This paper cites A survey on offline reinforcement learning: Taxonomy, review, and open problems,.

Hierarchical Diffusion Policy: manipulation trajectory generation via contact guidance A survey on offline reinforcement learning: Taxonomy, review, and open problems,

Reference 14

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Observation 66e077ef-ee35-4690-88ef-60ded81bba0b · outbound

This paper cites A hierarchical framework for long horizon planning of object-contact trajectories,.

Hierarchical Diffusion Policy: manipulation trajectory generation via contact guidance A hierarchical framework for long horizon planning of object-contact trajectories,

Reference 15

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation ba59fcc6-d0ce-485c-88f4-d43ff81fbbaa · outbound

This paper cites Enhancing dexterity in robotic manipulation via hierarchical contact exploration,.

Hierarchical Diffusion Policy: manipulation trajectory generation via contact guidance Enhancing dexterity in robotic manipulation via hierarchical contact exploration,

Reference 16

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Source-reported events for the cited work

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Observation e2140dd8-d17a-4a26-bfbe-fdba34a4b5d5 · outbound

This paper cites Hierarchical learning of robotic contact policies,.

Hierarchical Diffusion Policy: manipulation trajectory generation via contact guidance Hierarchical learning of robotic contact policies,

Reference 17

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Source-reported events for the cited work

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Observation 327c995f-9304-4ebe-86c6-22491d04f3a2 · outbound

This paper cites A learning based hierarchical control framework for human-robot collaboration,.

Hierarchical Diffusion Policy: manipulation trajectory generation via contact guidance A learning based hierarchical control framework for human-robot collaboration,

Reference 18

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Observation d09047ed-5de6-4cf6-b70b-17ffd3a564cf · outbound

This paper cites Multi-stage cable routing through hierarchical imitation learning,.

Hierarchical Diffusion Policy: manipulation trajectory generation via contact guidance Multi-stage cable routing through hierarchical imitation learning,

Reference 19

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Observation b0bc3aed-7092-428d-94f2-96637d402fa2 · outbound

This paper cites Bottom-up skill discovery from un- segmented demonstrations for long-horizon robot manipulation,.

Hierarchical Diffusion Policy: manipulation trajectory generation via contact guidance Bottom-up skill discovery from un- segmented demonstrations for long-horizon robot manipulation,

Reference 20

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Source-reported events for the cited work

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Observation 6f336398-5457-443e-af39-e38b9b82cfe6 · outbound

This paper cites Hierarchical reinforcement learning with universal policies for multi- step robotic manipulation,.

Hierarchical Diffusion Policy: manipulation trajectory generation via contact guidance Hierarchical reinforcement learning with universal policies for multi- step robotic manipulation,

Reference 21

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Source-reported events for the cited work

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Observation 86bf8875-435c-48b6-9e07-ccf3f17bf138 · outbound

This paper cites Residual skill policies: Learning an adaptable skill-based action space for rein- forcement learning for robotics,.

Hierarchical Diffusion Policy: manipulation trajectory generation via contact guidance Residual skill policies: Learning an adaptable skill-based action space for rein- forcement learning for robotics,

Reference 22

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Observation 0acd349b-6229-4781-9c61-8105f3524e6b · outbound

This paper cites Compositional foundation models for hierarchical planning,.

Hierarchical Diffusion Policy: manipulation trajectory generation via contact guidance Compositional foundation models for hierarchical planning,

Reference 23

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Observation 38b6a28d-14c2-4c2d-b77f-d54cf132e3bb · outbound

This paper cites Chain-of-Thought Predictive Control.

Hierarchical Diffusion Policy: manipulation trajectory generation via contact guidance Chain-of-Thought Predictive Control

Reference 24

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Observation 920fab1f-0cee-43b2-8633-ceaee4173899 · outbound

This paper cites Do as i can, not as i say: Grounding language in robotic affordances,.

Hierarchical Diffusion Policy: manipulation trajectory generation via contact guidance Do as i can, not as i say: Grounding language in robotic affordances,

Reference 25

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Source-reported events for the cited work

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Observation 91d4e149-2ff6-42e4-89af-e8927f2d165e · outbound

This paper cites Deep hierarchical planning from pixels,.

Hierarchical Diffusion Policy: manipulation trajectory generation via contact guidance Deep hierarchical planning from pixels,

Reference 26

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Observation 50d5c382-45a6-454d-b1dd-947d53a853b5 · outbound

This paper cites Adjacency constraint for efficient hierarchical reinforcement learning,.

Hierarchical Diffusion Policy: manipulation trajectory generation via contact guidance Adjacency constraint for efficient hierarchical reinforcement learning,

Reference 27

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Observation 457a6dbc-c35f-464b-97b9-c66a60abfe3b · outbound

This paper cites Planning irregular object packing via hierarchical reinforcement learning,.

Hierarchical Diffusion Policy: manipulation trajectory generation via contact guidance Planning irregular object packing via hierarchical reinforcement learning,

Reference 28

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation 636d834e-5c5f-42c2-8e00-fa8fbf5dce04 · outbound

This paper cites Hierarchical diffusion policy for kinematics-aware multi-task robotic manipulation,.

Hierarchical Diffusion Policy: manipulation trajectory generation via contact guidance Hierarchical diffusion policy for kinematics-aware multi-task robotic manipulation,

Reference 29

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation 0539c4a5-f8a0-4266-a59a-7cf91ee23f03 · outbound

This paper cites Multi-stage reinforcement learning for non-prehensile manipulation,.

Hierarchical Diffusion Policy: manipulation trajectory generation via contact guidance Multi-stage reinforcement learning for non-prehensile manipulation,

Reference 30

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Observation ceb14c30-2045-4839-93e2-b0680c388bb9 · outbound

This paper cites Deep imitation learning for complex manipulation tasks from virtual reality teleoperation,.

Hierarchical Diffusion Policy: manipulation trajectory generation via contact guidance Deep imitation learning for complex manipulation tasks from virtual reality teleoperation,

Reference 31

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation c95bf493-d13f-4924-bfd3-0bcecf7b5a5a · outbound

This paper cites Self-supervised correspon- dence in visuomotor policy learning,.

Hierarchical Diffusion Policy: manipulation trajectory generation via contact guidance Self-supervised correspon- dence in visuomotor policy learning,

Reference 32

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Unavailable: canonical work link unavailable.

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Observation af80af21-6800-4af5-b9a9-fe53049fdd94 · outbound

This paper cites The magical benchmark for robust imitation,.

Hierarchical Diffusion Policy: manipulation trajectory generation via contact guidance The magical benchmark for robust imitation,

Reference 33

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Unavailable: canonical work link unavailable.

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Observation 1d79ffd0-b775-4c8f-be1e-86b41596c7db · outbound

This paper cites Transporter networks: Rearranging the visual world for robotic manipulation,.

Hierarchical Diffusion Policy: manipulation trajectory generation via contact guidance Transporter networks: Rearranging the visual world for robotic manipulation,

Reference 34

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation 28fa7644-c76a-4874-ad28-4cde9c0710dd · outbound

This paper cites Speedfolding: Learning efficient bimanual folding of garments,.

Hierarchical Diffusion Policy: manipulation trajectory generation via contact guidance Speedfolding: Learning efficient bimanual folding of garments,

Reference 35

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Observation 84e6a813-d885-4d9a-a224-22a23f53e90d · outbound

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

Hierarchical Diffusion Policy: manipulation trajectory generation via contact guidance What matters in learning from offline human demonstrations for robot manipulation,

Reference 36

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verified fuzzy
raw_fallback, observed 2026-08-12T17:06:33.292236Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-12T17:06:32.345250Z digest=sha256:ec3b01a9fa3ecb0c3a89045bf13d25794d9ea53fa5cbb15425d49a8bea0c5ac4

Observation 9565120a-f40d-43de-9789-312bb14f482a · outbound

This paper cites Implicit behavioral cloning,.

Hierarchical Diffusion Policy: manipulation trajectory generation via contact guidance Implicit behavioral cloning,

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T17:06:33.272357Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-12T17:06:32.349244Z digest=sha256:c4f3a42cbaf046597f43247e516273a4b968d0caa3a14c8d6d07ddc191b762a0

Observation bf0cad7a-ae8b-4c32-a964-138ee738f99a · outbound

This paper cites Improved contrastive di- vergence training of energy-based models,.

Hierarchical Diffusion Policy: manipulation trajectory generation via contact guidance Improved contrastive di- vergence training of energy-based models,

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T17:06:33.252225Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-12T17:06:32.353317Z digest=sha256:a34f52e32755934265c30f9dc106c3026bf018d14dec3cd3d1e6ae53407ebf97

Observation c1ff14e4-7625-4861-be02-356cd918768a · outbound

This paper cites Learning the stein discrepancy for training and evaluating energy-based models without sampling,.

Hierarchical Diffusion Policy: manipulation trajectory generation via contact guidance Learning the stein discrepancy for training and evaluating energy-based models without sampling,

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T17:06:33.230947Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-12T17:06:32.357634Z digest=sha256:405b56aa90924b1528bda0acea05a634beffffe4ad079c4388018f215f65faff

Observation 5a82b327-f34e-453b-8009-1b527156cd45 · outbound

This paper cites Offline rein- forcement learning with realizability and single-policy concentrability,.

Hierarchical Diffusion Policy: manipulation trajectory generation via contact guidance Offline rein- forcement learning with realizability and single-policy concentrability,

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T17:06:33.214796Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-12T17:06:32.361757Z digest=sha256:09f6b763f1a0fdbfeb7a0174c02931ef97bae793618b25b06e7b8d1391d220c3

Observation 454e7fa1-518d-4d54-9c07-8f05273cffad · outbound

This paper cites Adversarially trained actor critic for offline reinforcement learning,.

Hierarchical Diffusion Policy: manipulation trajectory generation via contact guidance Adversarially trained actor critic for offline reinforcement learning,

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T17:06:33.196987Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-12T17:06:32.365669Z digest=sha256:dc58254780b9bb16fe5bd32976e7cc7b1a432588b5a7ca5b516907da19dcab2b

Observation d99d1077-755b-48c1-9265-59bd907dfbdb · outbound

This paper cites Action- quantized offline reinforcement learning for robotic skill learning,.

Hierarchical Diffusion Policy: manipulation trajectory generation via contact guidance Action- quantized offline reinforcement learning for robotic skill learning,

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T17:06:33.180486Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-12T17:06:32.369505Z digest=sha256:3398ea8e12cfba10998340bb407ed12826fe1a0599d102540f892dbc8de0d1c9

Observation 99084a4a-61fc-4e61-84ba-719e16b777d9 · outbound

This paper cites Off-policy deep reinforcement learning without exploration,.

Hierarchical Diffusion Policy: manipulation trajectory generation via contact guidance Off-policy deep reinforcement learning without exploration,

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T17:06:33.164448Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-12T17:06:32.373708Z digest=sha256:215c19697c34d321bce1993862ebfeb5c016b10dfdcb7771b2f6c6b5c066a512

Observation 7bf39d5d-eef0-485d-bd5b-9206ac0347d2 · outbound

This paper cites AWAC: Accelerating Online Reinforcement Learning with Offline Datasets.

Hierarchical Diffusion Policy: manipulation trajectory generation via contact guidance AWAC: Accelerating Online Reinforcement Learning with Offline Datasets

Reference 44

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unresolved
no resolver link, observed 2026-08-12T17:06:32.377820Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T17:06:32.377820Z digest=sha256:c1639591ad4e5fa32091eeeb17b7afb5e547f231355c802b55e2b53f08eaf9d7

Observation a59a16d2-9f78-434b-a07b-ed9fcd862532 · outbound

This paper cites Eligibility traces for off-policy policy evaluation,.

Hierarchical Diffusion Policy: manipulation trajectory generation via contact guidance Eligibility traces for off-policy policy evaluation,

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T17:06:33.148123Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-12T17:06:32.382692Z digest=sha256:838de3bfc865e6c5032ee9c42916fc79fffdbc250831151275145cc5928d1da0

Observation b6235c4b-fc81-4d03-8633-ed03fd3f715a · outbound

This paper cites Gendice: Generalized offline estimation of stationary values,.

Hierarchical Diffusion Policy: manipulation trajectory generation via contact guidance Gendice: Generalized offline estimation of stationary values,

Reference 46

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T17:06:33.132275Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-12T17:06:32.386931Z digest=sha256:20e4c749276cfeec864f81160b87dd97e375c9722db202c1f060c957864c251c

Observation 4805823e-39c6-4f43-a2da-4e424a060e9d · outbound

This paper cites Soft actor-critic: Off- policy maximum entropy deep reinforcement learning with a stochastic actor,.

Hierarchical Diffusion Policy: manipulation trajectory generation via contact guidance Soft actor-critic: Off- policy maximum entropy deep reinforcement learning with a stochastic actor,

Reference 47

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T17:06:33.112549Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-12T17:06:32.391442Z digest=sha256:a0f2700a8f07e9636de8163be1d924451295d341b7141a5d9a50c58eb33e06e0

Observation 89672416-044c-471c-9d02-9b6753f41768 · outbound

This paper cites Conservative q-learning for offline reinforcement learning,.

Hierarchical Diffusion Policy: manipulation trajectory generation via contact guidance Conservative q-learning for offline reinforcement learning,

Reference 48

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unresolved
no resolver link, observed 2026-08-12T17:06:32.395831Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T17:06:32.395831Z digest=sha256:ae8cc660206585923caf67624612e225d9b223f34652d598a588e08e16150495

Observation 238e0610-bc3c-4632-8e37-08a8783da8c8 · outbound

This paper cites An optimistic perspec- tive on offline reinforcement learning,.

Hierarchical Diffusion Policy: manipulation trajectory generation via contact guidance An optimistic perspec- tive on offline reinforcement learning,

Reference 49

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T17:06:33.077256Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-12T17:06:32.400190Z digest=sha256:c75f19dddf984cf18830cf729b80f1a7fc5f7bee1e575f9f029b0655d7265f25

Observation 9263abf8-a7c0-4b90-ab59-54e246d55c69 · outbound

This paper cites Cascaded diffusion models for high fidelity image generation,.

Hierarchical Diffusion Policy: manipulation trajectory generation via contact guidance Cascaded diffusion models for high fidelity image generation,

Reference 50

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no resolver link, observed 2026-08-12T17:06:32.404531Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T17:06:32.404531Z digest=sha256:0b004e8abfd6896d3d18944c89a71fc4a12312735c8545184c32a8fb94e973a8

Observation 30c1a7fe-0b46-4a82-b502-1bd1f4786ed9 · outbound

This paper cites Diffusion models beat gans on image synthesis,.

Hierarchical Diffusion Policy: manipulation trajectory generation via contact guidance Diffusion models beat gans on image synthesis,

Reference 51

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no resolver link, observed 2026-08-12T17:06:32.409206Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T17:06:32.409206Z digest=sha256:1e418f3f5776eb76a9d606d195837f3d7b37cb947420e04693a0a7420d1a6eb0

Observation 25547cbe-d316-4537-97a6-15c133dfee2e · outbound

This paper cites Tune-a-video: One-shot tuning of image diffusion models for text-to-video generation,.

Hierarchical Diffusion Policy: manipulation trajectory generation via contact guidance Tune-a-video: One-shot tuning of image diffusion models for text-to-video generation,

Reference 52

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T17:06:33.038751Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-12T17:06:32.414201Z digest=sha256:5e34facd49d0be0b5351b2fe4093a73cfc188358cc3eb3d811b3e89fe7e63442

Observation 02b5807e-5541-4398-8a25-ce04377f7471 · outbound

This paper cites Sora: A Review on Background, Technology, Limitations, and Opportunities of Large Vision Models.

Hierarchical Diffusion Policy: manipulation trajectory generation via contact guidance Sora: A Review on Background, Technology, Limitations, and Opportunities of Large Vision Models

Reference 53

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no resolver link, observed 2026-08-12T17:06:32.418783Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T17:06:32.418783Z digest=sha256:ef093c6c48f6bee1412cf7cfa1bd33e80b33f28fb491ccc7b0390a0a2ec5991c

Observation 62727111-53f5-4254-804c-6332a14e17f2 · outbound

This paper cites Lion: Latent point diffusion models for 3d shape generation,.

Hierarchical Diffusion Policy: manipulation trajectory generation via contact guidance Lion: Latent point diffusion models for 3d shape generation,

Reference 54

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T17:06:33.021570Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-12T17:06:32.423561Z digest=sha256:d64e1e86ea4892ac3ec5acf658f64b12fbc1c88be7ed9a96344eac4ed5f38024

Observation 580cfef9-b0a5-489c-864b-e317ce5d151a · outbound

This paper cites Diffusion probabilistic models for 3d point cloud generation,.

Hierarchical Diffusion Policy: manipulation trajectory generation via contact guidance Diffusion probabilistic models for 3d point cloud generation,

Reference 55

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T17:06:33.003593Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-12T17:06:32.428108Z digest=sha256:f7126ca3446fc2ec109cd8126233161b515a962c5fc28d99194f072aadd56b71

Observation 514b2512-a351-42a1-b4b9-bf9c5e32b37b · outbound

This paper cites Training Diffusion Models with Reinforcement Learning.

Hierarchical Diffusion Policy: manipulation trajectory generation via contact guidance Training Diffusion Models with Reinforcement Learning

Reference 56

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no resolver link, observed 2026-08-12T17:06:32.432605Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T17:06:32.432605Z digest=sha256:a59cf894da5f7417de363c146f14ef987f88ff252849f1efa6d3497e0994fa87

Observation cfab1a31-3a07-44b0-9f2e-8b8f6ba55a17 · outbound

This paper cites Is conditional generative modeling all you need for de- cision making?.

Hierarchical Diffusion Policy: manipulation trajectory generation via contact guidance Is conditional generative modeling all you need for de- cision making?

Reference 57

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T17:06:32.987260Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-12T17:06:32.437461Z digest=sha256:19d83a83f72e056a092dc80892952fa3af32a226bf94b82fb8c68bf5ab155a88

Observation 3c0a9de6-13c0-41a0-aba9-93721c357bed · outbound

This paper cites Crossway Diffusion: Improving Diffusion-based Visuomotor Policy via Self-supervised Learning.

Hierarchical Diffusion Policy: manipulation trajectory generation via contact guidance Crossway Diffusion: Improving Diffusion-based Visuomotor Policy via Self-supervised Learning

Reference 58

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T17:06:32.442090Z digest=sha256:15a4005bd150a71c635d3fefe0fe5e29beeaff037a45d283503e6ef6125b3874

Observation 99e2c951-9fe6-41d6-b8c1-e6c528473ed3 · outbound

This paper cites Classifier-Free Diffusion Guidance.

Hierarchical Diffusion Policy: manipulation trajectory generation via contact guidance Classifier-Free Diffusion Guidance

Reference 59

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no resolver link, observed 2026-08-12T17:06:32.446803Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T17:06:32.446803Z digest=sha256:2bb296bf12ad90e841ca4eb53aa1e992e948effcc538d6f8a77d8c98d868ca01

Observation 5a1e17bf-7c9b-4bbd-954d-226ea0a202fb · outbound

This paper cites Receding horizon control of nonlinear systems,.

Hierarchical Diffusion Policy: manipulation trajectory generation via contact guidance Receding horizon control of nonlinear systems,

Reference 60

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T17:06:32.971133Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-12T17:06:32.451174Z digest=sha256:bab91fb8706f7a76c39e947009000bab439f43eb7b36ece8455cabcf10d306f1

Observation 65736fb6-5a5e-4036-ac83-7ef99db1c2fe · outbound

This paper cites Pointnet: Deep learning on point sets for 3d classification and segmentation,.

Hierarchical Diffusion Policy: manipulation trajectory generation via contact guidance Pointnet: Deep learning on point sets for 3d classification and segmentation,

Reference 61

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T17:06:32.455455Z digest=sha256:e893b0b4c803cdd5a7cd112f5d122a6d739775cc990a953855d03e3aaa40bb01

Observation 52d9c586-abd6-4277-9c23-7cbb1a27244f · outbound

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

Hierarchical Diffusion Policy: manipulation trajectory generation via contact guidance Pointnet++: Deep hierarchical feature learning on point sets in a metric space,

Reference 62

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no resolver link, observed 2026-08-12T17:06:32.459974Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T17:06:32.459974Z digest=sha256:9584ccbc161cc7c082de42e229db2566923fce4bbdb001d09d4b32146d48e84d

Observation acb462c3-2d85-4872-b30c-3ab3b72e382d · outbound

This paper cites Diffusion policies for out-of- distribution generalization in offline reinforcement learning,.

Hierarchical Diffusion Policy: manipulation trajectory generation via contact guidance Diffusion policies for out-of- distribution generalization in offline reinforcement learning,

Reference 63

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T17:06:32.935009Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-12T17:06:32.464377Z digest=sha256:783ba73a7e037b6d1152da5255570e4bc6b92bd70126c7bfa7a1843ae2cd2c04

Observation 53636104-74cf-4d06-a5b9-7eaeb8b46c1a · outbound

This paper cites Behavior transformers: Cloning k modes with one stone,.

Hierarchical Diffusion Policy: manipulation trajectory generation via contact guidance Behavior transformers: Cloning k modes with one stone,

Reference 64

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T17:06:32.468864Z digest=sha256:21e053431523059df5500dcb3952bc87089bb524744ad04f8ffca450b0a419d9

Observation 98747b99-0511-43ce-8e56-615abb2b41e2 · outbound

This paper cites V oxposer: Composable 3d value maps for robotic manipulation with language models,.

Hierarchical Diffusion Policy: manipulation trajectory generation via contact guidance V oxposer: Composable 3d value maps for robotic manipulation with language models,

Reference 65

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verified fuzzy
raw_fallback, observed 2026-08-12T17:06:32.904766Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-12T17:06:32.473098Z digest=sha256:f7c200b47157db1a25319b414342acb8b47ffb234b0c80b6bc22f3e466194b7a

Observation 4e02b2af-4fb6-40fe-8272-0c781ca58000 · outbound

This paper cites Palm-e: An embodied multimodal language model,.

Hierarchical Diffusion Policy: manipulation trajectory generation via contact guidance Palm-e: An embodied multimodal language model,

Reference 66

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verified fuzzy
raw_fallback, observed 2026-08-12T17:06:32.885121Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-12T17:06:32.477077Z digest=sha256:b57a76d22bb1baddd3017dcd526fd2f8a00873fb6315b377e77d2ccae4b58c81

Observation c79e7312-9831-4bdd-9373-ce22ff68de34 · outbound

This paper cites Language models as zero-shot trajectory generators,.

Hierarchical Diffusion Policy: manipulation trajectory generation via contact guidance Language models as zero-shot trajectory generators,

Reference 67

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T17:06:32.481017Z digest=sha256:f36b8f77b4cc1c1fc412a354ba63504d04ec3228d044ce889f73541ad11b05b6

Observation 51f156b6-4866-43d7-adce-14bcd25ea016 · outbound

This paper cites On the sample complexity of actor-critic method for reinforcement learning with function approxima- tion,.

Hierarchical Diffusion Policy: manipulation trajectory generation via contact guidance On the sample complexity of actor-critic method for reinforcement learning with function approxima- tion,

Reference 68

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T17:06:32.866128Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-12T17:06:32.486014Z digest=sha256:a7f26036a5f03678dd6dccbb644ffaf1632651200f7c14349c1fc3f056591134

Observation c3301990-a4e4-4eb5-9cd7-35b65485df9c · outbound

This paper cites 3D Diffusion Policy: Generalizable Visuomotor Policy Learning via Simple 3D Representations.

Hierarchical Diffusion Policy: manipulation trajectory generation via contact guidance 3D Diffusion Policy: Generalizable Visuomotor Policy Learning via Simple 3D Representations

Reference 69

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no resolver link, observed 2026-08-12T17:06:32.490722Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T17:06:32.490722Z digest=sha256:119aed15622bf686acff19043003ff45de2e9f5ee7f935f25c0373e46822e9b8

Observation dd93b17a-b204-45c4-a18e-ab64f087222d · outbound

This paper cites On the continuity of rotation representations in neural networks,.

Hierarchical Diffusion Policy: manipulation trajectory generation via contact guidance On the continuity of rotation representations in neural networks,

Reference 70

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T17:06:32.495395Z digest=sha256:8fefaa3395e494d61525d81e18ef662e7466bd36bbde8b420c02ad761d42cf6c

Pith citing papers

No inbound Pith citation observations are available.