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Paper Citation Record · LEDGER

VolumeDP: Modeling Volumetric Representation for Manipulation Policy Learning

As of 15 August 2026, this Paper Citation Record lists 42 of 42 outbound references and 0 inbound Pith citation observations for arXiv:2603.17720.

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

pith.paper-citation-record.v1
2603.17720 v2

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measured 42 of 42 reference resolution

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measured 42 of 42 standing notices

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measured 0 of 0 inbound itemization

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42 of 42 outbound references displayed

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Outbound references

Observation a4137395-ab5f-4785-82f8-93474a9f745a · outbound

This paper cites Implicit behavioral cloning,.

VolumeDP: Modeling Volumetric Representation for Manipulation Policy Learning Implicit behavioral cloning,

Reference 1

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Observation b83df71a-d984-4c80-8ebd-17b144d79a7c · outbound

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

VolumeDP: Modeling Volumetric Representation for Manipulation Policy Learning Diffusion policy: Visuomotor policy learning via action diffusion,

Reference 2

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Observation 934c32c9-dbc8-4b43-be89-82d77dd20221 · outbound

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

VolumeDP: Modeling Volumetric Representation for Manipulation Policy Learning 3D Diffusion Policy: Generalizable Visuomotor Policy Learning via Simple 3D Representations

Reference 3

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Observation 4159f065-d677-42d7-a4f8-0b6f7b4d4b23 · outbound

This paper cites Act3D: 3D Feature Field Transformers for Multi-Task Robotic Manipulation.

VolumeDP: Modeling Volumetric Representation for Manipulation Policy Learning Act3D: 3D Feature Field Transformers for Multi-Task Robotic Manipulation

Reference 4

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Observation a696aa40-cfca-46a3-83f1-9e9535d9b85b · outbound

This paper cites Learning human-to-robot handovers from point clouds,.

VolumeDP: Modeling Volumetric Representation for Manipulation Policy Learning Learning human-to-robot handovers from point clouds,

Reference 5

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Observation 47c3a701-6839-41ca-a618-b5072b076e75 · outbound

This paper cites The ingredients for robotic diffusion transformers,.

VolumeDP: Modeling Volumetric Representation for Manipulation Policy Learning The ingredients for robotic diffusion transformers,

Reference 6

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Observation 90b99935-4041-45b5-b9ee-c654b86f6c32 · outbound

This paper cites Libero: Benchmarking knowledge transfer for lifelong robot learn- ing,.

VolumeDP: Modeling Volumetric Representation for Manipulation Policy Learning Libero: Benchmarking knowledge transfer for lifelong robot learn- ing,

Reference 7

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Observation fcd66c55-fe23-48ad-8f13-531b17af7be7 · outbound

This paper cites Using proba- bilistic movement primitives in robotics,.

VolumeDP: Modeling Volumetric Representation for Manipulation Policy Learning Using proba- bilistic movement primitives in robotics,

Reference 8

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Observation a028c151-1caa-440e-94c0-6514a7813dd7 · outbound

This paper cites Efficient reductions for imitation learning,.

VolumeDP: Modeling Volumetric Representation for Manipulation Policy Learning Efficient reductions for imitation learning,

Reference 9

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Observation 0e0d02b7-405b-4a38-8438-67e28a40c053 · outbound

This paper cites A reduction of imitation learning and structured prediction to no-regret online learning,.

VolumeDP: Modeling Volumetric Representation for Manipulation Policy Learning A reduction of imitation learning and structured prediction to no-regret online learning,

Reference 10

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Observation 9bf4e198-5978-4a25-80d0-f191a1bd1d3d · outbound

This paper cites Domain generalization: A survey,.

VolumeDP: Modeling Volumetric Representation for Manipulation Policy Learning Domain generalization: A survey,

Reference 11

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Observation 5224687c-8b3d-4d66-99ee-d2e4710b2f5d · outbound

This paper cites What Matters in Learning from Offline Human Demonstrations for Robot Manipulation.

VolumeDP: Modeling Volumetric Representation for Manipulation Policy Learning What Matters in Learning from Offline Human Demonstrations for Robot Manipulation

Reference 12

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Observation 13bae356-0f9a-4094-b2ce-0e0ac08343fc · outbound

This paper cites Behavior transformers: Cloningkmodes with one stone,.

VolumeDP: Modeling Volumetric Representation for Manipulation Policy Learning Behavior transformers: Cloningkmodes with one stone,

Reference 13

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Observation 748f1b1f-7780-4104-a4d4-24287e950f17 · outbound

This paper cites Spatial action maps for mobile manipulation,.

VolumeDP: Modeling Volumetric Representation for Manipulation Policy Learning Spatial action maps for mobile manipulation,

Reference 14

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Observation 892ae3d1-120b-49d4-8211-0eb1e70d6144 · outbound

This paper cites Diffusion model-augmented behavioral cloning,.

VolumeDP: Modeling Volumetric Representation for Manipulation Policy Learning Diffusion model-augmented behavioral cloning,

Reference 15

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Observation 1d91d114-4d93-4aca-add9-3c755080977e · outbound

This paper cites Denoising diffusion probabilistic models,.

VolumeDP: Modeling Volumetric Representation for Manipulation Policy Learning Denoising diffusion probabilistic models,

Reference 16

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Observation 874ddbb6-af72-468f-afb8-270c66c17782 · outbound

This paper cites Score-Based Generative Modeling through Stochastic Differential Equations.

VolumeDP: Modeling Volumetric Representation for Manipulation Policy Learning Score-Based Generative Modeling through Stochastic Differential Equations

Reference 17

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Observation 1dce8ba4-60bb-4d4a-96bf-102f991f00e2 · outbound

This paper cites Denoising Diffusion Implicit Models.

VolumeDP: Modeling Volumetric Representation for Manipulation Policy Learning Denoising Diffusion Implicit Models

Reference 18

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Observation f3f97b1c-dfa0-4295-89ed-4140a0f31a20 · outbound

This paper cites Crossway diffu- sion: Improving diffusion-based visuomotor policy via self-supervised learning,.

VolumeDP: Modeling Volumetric Representation for Manipulation Policy Learning Crossway diffu- sion: Improving diffusion-based visuomotor policy via self-supervised learning,

Reference 19

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Observation 8dd1ca45-4644-47dd-b1d5-0c132630ff22 · outbound

This paper cites Octo: An open-source generalist robot policy,.

VolumeDP: Modeling Volumetric Representation for Manipulation Policy Learning Octo: An open-source generalist robot policy,

Reference 20

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Observation 88bf4f8e-b9b7-4af8-bacc-b6fe49955a1c · outbound

This paper cites Aloha unleashed: A simple recipe for robot dexterity,.

VolumeDP: Modeling Volumetric Representation for Manipulation Policy Learning Aloha unleashed: A simple recipe for robot dexterity,

Reference 21

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Observation ff837423-db9b-458f-814b-912b6dd4e200 · outbound

This paper cites OpenVLA: An Open-Source Vision-Language-Action Model.

VolumeDP: Modeling Volumetric Representation for Manipulation Policy Learning OpenVLA: An Open-Source Vision-Language-Action Model

Reference 22

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Observation e508af1e-fd0f-4275-879f-dae53ad9d63d · outbound

This paper cites Perceiver-actor: A multi- task transformer for robotic manipulation,.

VolumeDP: Modeling Volumetric Representation for Manipulation Policy Learning Perceiver-actor: A multi- task transformer for robotic manipulation,

Reference 23

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Observation 49333755-ed0a-4598-980a-1fbf953f2306 · outbound

This paper cites Gnfactor: Multi-task real robot learning with generalizable neural feature fields,.

VolumeDP: Modeling Volumetric Representation for Manipulation Policy Learning Gnfactor: Multi-task real robot learning with generalizable neural feature fields,

Reference 24

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Observation 388b40f9-956b-4e8c-b25e-dd8487f2aa2d · outbound

This paper cites Rvt: Robotic view transformer for 3d object manipulation,.

VolumeDP: Modeling Volumetric Representation for Manipulation Policy Learning Rvt: Robotic view transformer for 3d object manipulation,

Reference 25

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Observation bea649c3-5272-45c0-abcd-46ec540aa07b · outbound

This paper cites Deformable DETR: Deformable Transformers for End-to-End Object Detection.

VolumeDP: Modeling Volumetric Representation for Manipulation Policy Learning Deformable DETR: Deformable Transformers for End-to-End Object Detection

Reference 26

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Observation 89974dad-0b5b-4722-8c1b-86cae44a48a9 · outbound

This paper cites Bev- former: learning bird’s-eye-view representation from lidar-camera via spatiotemporal transformers,.

VolumeDP: Modeling Volumetric Representation for Manipulation Policy Learning Bev- former: learning bird’s-eye-view representation from lidar-camera via spatiotemporal transformers,

Reference 27

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Observation 308ba305-776a-4a59-bcb1-dfe04f5b19de · outbound

This paper cites Occ3d: A large-scale 3d occupancy prediction benchmark for autonomous driving,.

VolumeDP: Modeling Volumetric Representation for Manipulation Policy Learning Occ3d: A large-scale 3d occupancy prediction benchmark for autonomous driving,

Reference 28

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Observation fc0de2c0-a0f4-4aac-b32e-1d3093bab5f0 · outbound

This paper cites Cvt-occ: Cost volume temporal fusion for 3d occupancy prediction,.

VolumeDP: Modeling Volumetric Representation for Manipulation Policy Learning Cvt-occ: Cost volume temporal fusion for 3d occupancy prediction,

Reference 29

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Observation f7fd6b00-9dba-42bb-ba90-6b13e2f598ee · outbound

This paper cites DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter.

VolumeDP: Modeling Volumetric Representation for Manipulation Policy Learning DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter

Reference 30

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Observation 165234c0-d7f8-4856-8725-9299041fa718 · outbound

This paper cites TokenLearner: What Can 8 Learned Tokens Do for Images and Videos?.

VolumeDP: Modeling Volumetric Representation for Manipulation Policy Learning TokenLearner: What Can 8 Learned Tokens Do for Images and Videos?

Reference 31

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Observation 030fcf50-ba7b-4039-983c-8cd44b776895 · outbound

This paper cites Rethinking Latent Redundancy in Behavior Cloning: An Information Bottleneck Approach for Robot Manipulation.

VolumeDP: Modeling Volumetric Representation for Manipulation Policy Learning Rethinking Latent Redundancy in Behavior Cloning: An Information Bottleneck Approach for Robot Manipulation

Reference 32

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Observation af1251f1-af6d-41f1-b4da-b00684610ede · outbound

This paper cites Maniskill3: Gpu parallelized robot simulation and rendering for generalizable embodied ai,.

VolumeDP: Modeling Volumetric Representation for Manipulation Policy Learning Maniskill3: Gpu parallelized robot simulation and rendering for generalizable embodied ai,

Reference 33

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Observation 33971fa3-c89b-4c4a-ab5a-a9db8cd88a04 · outbound

This paper cites LIBERO-Plus: In-depth Robustness Analysis of Vision-Language-Action Models.

VolumeDP: Modeling Volumetric Representation for Manipulation Policy Learning LIBERO-Plus: In-depth Robustness Analysis of Vision-Language-Action Models

Reference 34

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Observation 70223918-f4e2-48b3-b8d3-72d0a262af70 · outbound

This paper cites RDT-1B: a Diffusion Foundation Model for Bimanual Manipulation.

VolumeDP: Modeling Volumetric Representation for Manipulation Policy Learning RDT-1B: a Diffusion Foundation Model for Bimanual Manipulation

Reference 35

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Observation 254afa48-c502-4b35-ae74-3827c9e0af7f · outbound

This paper cites Multimodal Diffusion Transformer: Learning Versatile Behavior from Multimodal Goals.

VolumeDP: Modeling Volumetric Representation for Manipulation Policy Learning Multimodal Diffusion Transformer: Learning Versatile Behavior from Multimodal Goals

Reference 36

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Observation 430c4597-8764-44ed-bc15-35fc2c792ea6 · outbound

This paper cites Any-point Trajectory Modeling for Policy Learning.

VolumeDP: Modeling Volumetric Representation for Manipulation Policy Learning Any-point Trajectory Modeling for Policy Learning

Reference 37

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Observation 72616562-f47a-40cd-9545-907c86ec6225 · outbound

This paper cites Translating flow to policy via hindsight online imitation,.

VolumeDP: Modeling Volumetric Representation for Manipulation Policy Learning Translating flow to policy via hindsight online imitation,

Reference 38

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source=pdf_text observed=2026-07-13T23:00:31.128534Z digest=sha256:ee6f42f58cd81a37c35467f27933a47d6f9381bf25187321a6d1464fbc7f3fa2

Observation e2f4bbe2-5260-491e-a60c-e171cdf3776b · outbound

This paper cites DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset.

VolumeDP: Modeling Volumetric Representation for Manipulation Policy Learning DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset

Reference 39

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source=pdf_text observed=2026-07-13T23:00:31.128534Z digest=sha256:2b5ae7fff921118d8cafc590d1dcdca4ed38807e21f045e356b56a0198521488

Observation df66a9df-45c6-4fb4-9892-bfeca83927d4 · outbound

This paper cites Vilt: Vision-and-language transformer without convolution or region supervision,.

VolumeDP: Modeling Volumetric Representation for Manipulation Policy Learning Vilt: Vision-and-language transformer without convolution or region supervision,

Reference 40

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Observation 78ce29e0-7568-47db-a10d-2c37425db870 · outbound

This paper cites Galaxea Open-World Dataset and G0 Dual-System VLA Model.

VolumeDP: Modeling Volumetric Representation for Manipulation Policy Learning Galaxea Open-World Dataset and G0 Dual-System VLA Model

Reference 41

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Observation 9b90a32b-a90c-4122-aa9e-f25139cb3fb4 · outbound

This paper cites Gello: A general, low- cost, and intuitive teleoperation framework for robot manipulators,.

VolumeDP: Modeling Volumetric Representation for Manipulation Policy Learning Gello: A general, low- cost, and intuitive teleoperation framework for robot manipulators,

Reference 42

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no resolver link, observed 2026-07-13T23:00:31.128534Z

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source=pdf_text observed=2026-07-13T23:00:31.128534Z digest=sha256:81fe68a1bef3255b32cc4e9b750f31c5992f2f139041679f527a6eb07d85bbf0

Pith citing papers

No inbound Pith citation observations are available.