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Paper Citation Record · LEDGER

Decompose and Reorganize: Planning with Primitives and Visuomotor Policies Learned from Demonstrations

As of 22 August 2026, this Paper Citation Record lists 92 of 92 outbound references and 0 inbound Pith citation observations for arXiv:2607.25397.

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

pith.paper-citation-record.v1
2607.25397 v1

Coverage vector

measured 92 of 92 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-01T02:37:40.555320Z

measured 92 of 92 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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Reference resolution

92 of 92 outbound references displayed

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Outbound references

Observation 64565c1b-d447-41a5-abb9-f234511a2a17 · outbound

This paper cites An incremental constraint-based framework for task and motion planning,.

Decompose and Reorganize: Planning with Primitives and Visuomotor Policies Learned from Demonstrations An incremental constraint-based framework for task and motion planning,

Reference 1

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Observation 8c96daaa-f7f7-4bbb-aa70-a80586d95e88 · outbound

This paper cites Task and motion planning for execution in the real,.

Decompose and Reorganize: Planning with Primitives and Visuomotor Policies Learned from Demonstrations Task and motion planning for execution in the real,

Reference 2

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Observation 8d67ee05-c010-4a41-a503-8d6e663427e5 · outbound

This paper cites Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware,.

Decompose and Reorganize: Planning with Primitives and Visuomotor Policies Learned from Demonstrations Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware,

Reference 3

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Observation e608ef1a-91a1-4b5b-b75a-dab3ace2ba00 · outbound

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

Decompose and Reorganize: Planning with Primitives and Visuomotor Policies Learned from Demonstrations GELLO: A general, low-cost, and intuitive teleoperation framework for robot manipulators,

Reference 4

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Observation b195b0cf-3e20-41f1-8e71-405281ef60bf · outbound

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

Decompose and Reorganize: Planning with Primitives and Visuomotor Policies Learned from Demonstrations Diffusion Policy: Visuomotor Policy Learning via Action Diffusion,

Reference 5

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source=pdf_text observed=2026-08-01T02:37:40.241127Z digest=sha256:a09b89585e80976a82eb94b7b5297f27a3e52a07fbdaaa1e1a88f6039c124171

Observation 9b8dcb17-c92f-455c-8682-5b34f107838f · outbound

This paper cites Implicit behavioral cloning,.

Decompose and Reorganize: Planning with Primitives and Visuomotor Policies Learned from Demonstrations Implicit behavioral cloning,

Reference 6

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Observation 1c55e254-b80c-40f4-9367-2e6560d0b6ab · outbound

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

Decompose and Reorganize: Planning with Primitives and Visuomotor Policies Learned from Demonstrations What matters in learning from offline human demonstrations for robot manipulation,

Reference 7

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source=pdf_text observed=2026-08-01T02:37:40.249128Z digest=sha256:f845ca838b3956a846eeb88eb13a13b247c61097f705f1f4acd46e6977cbc272

Observation d017bd0c-2ec9-49ae-9fea-0003da6b825c · outbound

This paper cites 3D Diffuser Actor: Policy diffusion with 3d scene representations,.

Decompose and Reorganize: Planning with Primitives and Visuomotor Policies Learned from Demonstrations 3D Diffuser Actor: Policy diffusion with 3d scene representations,

Reference 8

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source=pdf_text observed=2026-08-01T02:37:40.252648Z digest=sha256:34443367515dcf10558fae72e83e59720ac3525b69b1e8bb0ebc7aabfc5eb360

Observation a643d6b1-ac76-42c8-ae92-bb0ee2ece03a · outbound

This paper cites 3D Diffusion Policy: Generalizable visuomotor policy learning via simple 3D repre- sentations,.

Decompose and Reorganize: Planning with Primitives and Visuomotor Policies Learned from Demonstrations 3D Diffusion Policy: Generalizable visuomotor policy learning via simple 3D repre- sentations,

Reference 9

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source=pdf_text observed=2026-08-01T02:37:40.256056Z digest=sha256:e13ba1f19e66ee7beae10d856740fec0c99e7db638033eda0ebe1fd6de485c5e

Observation 7947aac2-6454-44f8-803c-0685a1abc89d · outbound

This paper cites π0: A vision-language-action flow model for general robot control,.

Decompose and Reorganize: Planning with Primitives and Visuomotor Policies Learned from Demonstrations π0: A vision-language-action flow model for general robot control,

Reference 10

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Observation 54d0cf6d-7997-4af6-9186-abad47a6a0ed · outbound

This paper cites ALOHA Unleashed: A simple recipe for robot dexterity,.

Decompose and Reorganize: Planning with Primitives and Visuomotor Policies Learned from Demonstrations ALOHA Unleashed: A simple recipe for robot dexterity,

Reference 11

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Observation e5b46b0f-56fc-4f87-9e19-cb974989debb · outbound

This paper cites RDT-1B: A diffusion foundation model for bimanual manipulation,.

Decompose and Reorganize: Planning with Primitives and Visuomotor Policies Learned from Demonstrations RDT-1B: A diffusion foundation model for bimanual manipulation,

Reference 12

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Observation 8ab58bd1-06e3-45df-8c87-d0a6d25b0887 · outbound

This paper cites A careful exami- nation of large behavior models for multitask dexterous manipulation,.

Decompose and Reorganize: Planning with Primitives and Visuomotor Policies Learned from Demonstrations A careful exami- nation of large behavior models for multitask dexterous manipulation,

Reference 13

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Observation b125c2b6-2bbd-4dbb-b50f-15424ec767a2 · outbound

This paper cites A Bimanual Manipulation Taxonomy,.

Decompose and Reorganize: Planning with Primitives and Visuomotor Policies Learned from Demonstrations A Bimanual Manipulation Taxonomy,

Reference 14

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Observation 8b09baf7-c25d-44de-8e1d-f2759054879e · outbound

This paper cites BiKC: Keypose-conditioned consistency policy for bimanual robotic manipulation,.

Decompose and Reorganize: Planning with Primitives and Visuomotor Policies Learned from Demonstrations BiKC: Keypose-conditioned consistency policy for bimanual robotic manipulation,

Reference 15

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source=pdf_text observed=2026-08-01T02:37:40.277328Z digest=sha256:44e920983e5fbd0ff12f3f4657151f7769eee94c6cc7e4ba3b9afcccc9448cee

Observation e0701080-93b1-438f-8558-1c4ba3f0bfb6 · outbound

This paper cites Combined task and motion planning for a dual-arm robot to use a suction cup tool,.

Decompose and Reorganize: Planning with Primitives and Visuomotor Policies Learned from Demonstrations Combined task and motion planning for a dual-arm robot to use a suction cup tool,

Reference 16

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Observation 677de3aa-9eb6-4c86-ae77-31043fe49b56 · outbound

This paper cites Enabling versatility and dexterity of the dual- arm manipulators: A general framework toward universal cooperative manipulation,.

Decompose and Reorganize: Planning with Primitives and Visuomotor Policies Learned from Demonstrations Enabling versatility and dexterity of the dual- arm manipulators: A general framework toward universal cooperative manipulation,

Reference 17

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Observation 03a3cdc7-bebb-496c-98fc-0fda95e9c15e · outbound

This paper cites Efficient task/motion planning for a dual-arm robot from language instructions and cooking images,.

Decompose and Reorganize: Planning with Primitives and Visuomotor Policies Learned from Demonstrations Efficient task/motion planning for a dual-arm robot from language instructions and cooking images,

Reference 18

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Observation e38a4585-13d8-46b6-8372-a3fa6b8e6244 · outbound

This paper cites Bi-KVIL: Keypoints-based Visual Imitation Learning of Bimanual Manipulation Tasks,.

Decompose and Reorganize: Planning with Primitives and Visuomotor Policies Learned from Demonstrations Bi-KVIL: Keypoints-based Visual Imitation Learning of Bimanual Manipulation Tasks,

Reference 19

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Observation 2deeeebc-3cfa-4418-84fe-36eb57250f64 · outbound

This paper cites Gripper keypose and object pointflow as interfaces for bimanual robotic manipulation,.

Decompose and Reorganize: Planning with Primitives and Visuomotor Policies Learned from Demonstrations Gripper keypose and object pointflow as interfaces for bimanual robotic manipulation,

Reference 20

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Observation 1a3a59a2-9803-4449-9e75-3f05097c6115 · outbound

This paper cites Dexmimicgen: Automated data generation for bimanual dexterous manipulation via imitation learning,.

Decompose and Reorganize: Planning with Primitives and Visuomotor Policies Learned from Demonstrations Dexmimicgen: Automated data generation for bimanual dexterous manipulation via imitation learning,

Reference 21

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Observation 5b1bfb8a-9b13-4253-bf6b-c2528e3e7636 · outbound

This paper cites Mimicgen: A data generation system for scalable robot learning using human demonstrations,.

Decompose and Reorganize: Planning with Primitives and Visuomotor Policies Learned from Demonstrations Mimicgen: A data generation system for scalable robot learning using human demonstrations,

Reference 22

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Observation fca68437-730c-448c-821c-764d50f98a06 · outbound

This paper cites RoboTwin 2.0: A Scalable Data Generator and Benchmark with Strong Domain Randomization for Robust Bimanual Robotic Manipulation.

Decompose and Reorganize: Planning with Primitives and Visuomotor Policies Learned from Demonstrations RoboTwin 2.0: A Scalable Data Generator and Benchmark with Strong Domain Randomization for Robust Bimanual Robotic Manipulation

Reference 23

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source=pdf_text observed=2026-08-01T02:37:40.307174Z digest=sha256:5f08a5540f3ebc49f199d0f7582a43ce7f93cfe49f1e1b7be9af28f6dd3ca25c

Observation e0e47f1a-89d9-4959-bcec-7d9fe0026d1a · outbound

This paper cites Novel demonstration generation with gaussian splatting enables robust one-shot manipulation,.

Decompose and Reorganize: Planning with Primitives and Visuomotor Policies Learned from Demonstrations Novel demonstration generation with gaussian splatting enables robust one-shot manipulation,

Reference 24

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Observation 7f17affc-5d26-41f2-874f-0a135e01882c · outbound

This paper cites Equibot: SIM(3)-Equivariant diffusion policy for generalizable and data efficient learning,.

Decompose and Reorganize: Planning with Primitives and Visuomotor Policies Learned from Demonstrations Equibot: SIM(3)-Equivariant diffusion policy for generalizable and data efficient learning,

Reference 25

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Observation 4e290bf7-a04f-4130-b263-4cb7ccdd6ef2 · outbound

This paper cites Demogen: Synthetic demonstration generation for data-efficient visuomotor policy learning,.

Decompose and Reorganize: Planning with Primitives and Visuomotor Policies Learned from Demonstrations Demogen: Synthetic demonstration generation for data-efficient visuomotor policy learning,

Reference 26

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Observation 8ae04bbe-5e64-40fa-aa96-8ebd800fb42e · outbound

This paper cites You only teach once: Learn one-shot bimanual robotic manipulation from video demonstrations,.

Decompose and Reorganize: Planning with Primitives and Visuomotor Policies Learned from Demonstrations You only teach once: Learn one-shot bimanual robotic manipulation from video demonstrations,

Reference 27

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Observation 27697461-e729-494c-ba56-6a10c5bfe4ff · outbound

This paper cites Diffusion-EDFs: Bi-Equivariant denoising generative modeling on SE(3) for visual robotic manipulation,.

Decompose and Reorganize: Planning with Primitives and Visuomotor Policies Learned from Demonstrations Diffusion-EDFs: Bi-Equivariant denoising generative modeling on SE(3) for visual robotic manipulation,

Reference 28

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source=pdf_text observed=2026-08-01T02:37:40.326322Z digest=sha256:221af05e625bb94805a0d21c9e56d76158065678a18c77054ec63b5d392053d1

Observation 535aa422-603f-484b-a219-3e372952089a · outbound

This paper cites RiEMann: Near real-time SE(3)-equivariant robot manipulation without point cloud segmentation,.

Decompose and Reorganize: Planning with Primitives and Visuomotor Policies Learned from Demonstrations RiEMann: Near real-time SE(3)-equivariant robot manipulation without point cloud segmentation,

Reference 29

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Observation 1fade135-672f-4433-b86a-5c422e1a4d1e · outbound

This paper cites 3d equivariant visuomotor policy learning via spherical projection,.

Decompose and Reorganize: Planning with Primitives and Visuomotor Policies Learned from Demonstrations 3d equivariant visuomotor policy learning via spherical projection,

Reference 30

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Observation 419d4795-5175-4d74-b5a1-56dcb4147360 · outbound

This paper cites A practical guide for incorporating symmetry in diffusion policy,.

Decompose and Reorganize: Planning with Primitives and Visuomotor Policies Learned from Demonstrations A practical guide for incorporating symmetry in diffusion policy,

Reference 31

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Observation 3701b736-144c-4279-820c-2e68aa8d6725 · outbound

This paper cites Plan-seq- learn: Language model guided rl for solving long horizon robotics tasks,.

Decompose and Reorganize: Planning with Primitives and Visuomotor Policies Learned from Demonstrations Plan-seq- learn: Language model guided rl for solving long horizon robotics tasks,

Reference 32

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Observation c713c68b-599c-4710-900e-92a83d407455 · outbound

This paper cites League: Guided skill learning and abstraction for long-horizon manipulation,.

Decompose and Reorganize: Planning with Primitives and Visuomotor Policies Learned from Demonstrations League: Guided skill learning and abstraction for long-horizon manipulation,

Reference 33

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Observation 003642ad-1653-4f92-9fbc-2e6bff0239b8 · outbound

This paper cites STAP: Sequencing task- agnostic policies,.

Decompose and Reorganize: Planning with Primitives and Visuomotor Policies Learned from Demonstrations STAP: Sequencing task- agnostic policies,

Reference 34

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Observation 37cfb41b-fece-44c7-b050-9dc3a59568ed · outbound

This paper cites Logic-skill program- ming: An optimization-based approach to sequential skill planning,.

Decompose and Reorganize: Planning with Primitives and Visuomotor Policies Learned from Demonstrations Logic-skill program- ming: An optimization-based approach to sequential skill planning,

Reference 35

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Observation 8b9c6228-6c96-4650-b283-f32a21b09f61 · outbound

This paper cites Spire: Synergistic planning, imitation, and reinforcement learning for long- horizon manipulation,.

Decompose and Reorganize: Planning with Primitives and Visuomotor Policies Learned from Demonstrations Spire: Synergistic planning, imitation, and reinforcement learning for long- horizon manipulation,

Reference 36

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Observation fc0c8911-a88f-47e7-8127-06c231abe3c8 · outbound

This paper cites NOD-TAMP: Multi- step manipulation planning with neural object descriptors,.

Decompose and Reorganize: Planning with Primitives and Visuomotor Policies Learned from Demonstrations NOD-TAMP: Multi- step manipulation planning with neural object descriptors,

Reference 37

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Observation 1c32ea6c-7e72-433b-a4ee-a68791335210 · outbound

This paper cites Logic learning from demonstrations for multi-step manipulation tasks in dynamic environ- ments,.

Decompose and Reorganize: Planning with Primitives and Visuomotor Policies Learned from Demonstrations Logic learning from demonstrations for multi-step manipulation tasks in dynamic environ- ments,

Reference 38

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Observation b402d492-edb2-4b34-b63f-10275b1680cb · outbound

This paper cites Generative factor chaining: Coor- dinated manipulation with diffusion-based factor graph,.

Decompose and Reorganize: Planning with Primitives and Visuomotor Policies Learned from Demonstrations Generative factor chaining: Coor- dinated manipulation with diffusion-based factor graph,

Reference 39

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Observation 9c6ae041-d77d-4030-8205-04c7b08d99e9 · outbound

This paper cites Human-in-the-loop task and motion planning for imitation learning,.

Decompose and Reorganize: Planning with Primitives and Visuomotor Policies Learned from Demonstrations Human-in-the-loop task and motion planning for imitation learning,

Reference 40

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Observation dc94ac19-8385-4658-8934-e06d6d5a93d7 · outbound

This paper cites Skillmimicgen: Automated demonstration generation for efficient skill learning and deployment,.

Decompose and Reorganize: Planning with Primitives and Visuomotor Policies Learned from Demonstrations Skillmimicgen: Automated demonstration generation for efficient skill learning and deployment,

Reference 41

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source=pdf_text observed=2026-08-01T02:37:40.371710Z digest=sha256:c84b47d694d358bc0868570bee2a63f9873f4c7315991e62853f3c9a2720b8b7

Observation 33960a3a-78d7-4338-8abd-dec90bdc54c2 · outbound

This paper cites Learning compositional behaviors from demonstration and language,.

Decompose and Reorganize: Planning with Primitives and Visuomotor Policies Learned from Demonstrations Learning compositional behaviors from demonstration and language,

Reference 42

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Observation 76d9b9c0-c2b3-472c-9c94-7908e611b032 · outbound

This paper cites Learning a thousand tasks in a day,.

Decompose and Reorganize: Planning with Primitives and Visuomotor Policies Learned from Demonstrations Learning a thousand tasks in a day,

Reference 43

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Observation b4e987dd-7190-4a07-b064-fce4bdb79a16 · outbound

This paper cites Learning Reusable Manipulation Strategies,.

Decompose and Reorganize: Planning with Primitives and Visuomotor Policies Learned from Demonstrations Learning Reusable Manipulation Strategies,

Reference 44

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source=pdf_text observed=2026-08-01T02:37:40.382252Z digest=sha256:c0ffc20aef0a52089f0e07e0615b0aa6b8523f20646951db36032dc83aee248b

Observation 3918752e-a35c-49af-9ef7-54658d79abb7 · outbound

This paper cites InterPreT: Interactive predicate learning from language feedback for generalizable task planning,.

Decompose and Reorganize: Planning with Primitives and Visuomotor Policies Learned from Demonstrations InterPreT: Interactive predicate learning from language feedback for generalizable task planning,

Reference 45

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source=pdf_text observed=2026-08-01T02:37:40.385747Z digest=sha256:0e39a3cda4d9ba21c16e2ea8a36cc2455e5d32b08ee988cb90fa499ad6d6bd83

Observation adde28e8-1050-4c71-a206-2412e5bbc3b6 · outbound

This paper cites UniDomain: Pretraining a unified PDDL domain from real-world demonstrations for generalizable robot task planning,.

Decompose and Reorganize: Planning with Primitives and Visuomotor Policies Learned from Demonstrations UniDomain: Pretraining a unified PDDL domain from real-world demonstrations for generalizable robot task planning,

Reference 46

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source=pdf_text observed=2026-08-01T02:37:40.389293Z digest=sha256:46fd6d85dae7d13c1523f2f9fbbc85eaa06c07c4abbdef50df92ef6eab512405

Observation 68ab13e8-188d-4e2a-85f2-dbe70048d8c1 · outbound

This paper cites Vlm see, robot do: Human demo video to robot action plan via vision language model,.

Decompose and Reorganize: Planning with Primitives and Visuomotor Policies Learned from Demonstrations Vlm see, robot do: Human demo video to robot action plan via vision language model,

Reference 47

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source=pdf_text observed=2026-08-01T02:37:40.393117Z digest=sha256:c76903ad0493071944861d426420cab5369fac0957b6ca6d7d4b3b30e894beba

Observation 589c10cb-66c8-4c3c-bf93-bb634f4f220f · outbound

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

Decompose and Reorganize: Planning with Primitives and Visuomotor Policies Learned from Demonstrations Do as i can, not as i say: Grounding language in robotic affordances,

Reference 48

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source=pdf_text observed=2026-08-01T02:37:40.396662Z digest=sha256:98176677a92d245e9ad8d049f2d63a6bd30f5a334f75492d3558f74a06c290f6

Observation 4b1312bb-3b8e-4240-882f-54c8e5ebf083 · outbound

This paper cites Progprompt: Generating situated robot task plans using large language models,.

Decompose and Reorganize: Planning with Primitives and Visuomotor Policies Learned from Demonstrations Progprompt: Generating situated robot task plans using large language models,

Reference 49

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source=pdf_text observed=2026-08-01T02:37:40.400136Z digest=sha256:976cd5ea8385797ff06a0edff6ba7af4964ce0ad8b900375df23604c86fa1c7e

Observation d385d11d-bf74-45f3-ad09-54579df0b9d9 · outbound

This paper cites Differentiable physics and stable modes for tool-use and manipulation planning,.

Decompose and Reorganize: Planning with Primitives and Visuomotor Policies Learned from Demonstrations Differentiable physics and stable modes for tool-use and manipulation planning,

Reference 50

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source=pdf_text observed=2026-08-01T02:37:40.403572Z digest=sha256:4cee5d922a5c247a01144ffec505a270850237b469c67325595a9c0e03dc7c8b

Observation 133c0150-dea3-4efb-b808-da3c3efe2ead · outbound

This paper cites Object-centric task and motion planning in dynamic environments,.

Decompose and Reorganize: Planning with Primitives and Visuomotor Policies Learned from Demonstrations Object-centric task and motion planning in dynamic environments,

Reference 51

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source=pdf_text observed=2026-08-01T02:37:40.407474Z digest=sha256:69a2a1102f927bba511cc42b9cbc5a6c33ca20e9e9df0ae565ef23b83019cd4b

Observation 74d7af55-8bfb-4dfd-9796-57146ba4a650 · outbound

This paper cites Sampling-based methods for factored task and motion planning,.

Decompose and Reorganize: Planning with Primitives and Visuomotor Policies Learned from Demonstrations Sampling-based methods for factored task and motion planning,

Reference 52

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source=pdf_text observed=2026-08-01T02:37:40.411407Z digest=sha256:1e292f24fd704df2a4e4aeed60f17097ca947b99f0beb0a0cec78f17c8c4d5cc

Observation 47d52902-7712-47e7-a8c2-fc9099a04880 · outbound

This paper cites PDDLStream: Integrating symbolic planners and blackbox sam- plers via optimistic adaptive planning,.

Decompose and Reorganize: Planning with Primitives and Visuomotor Policies Learned from Demonstrations PDDLStream: Integrating symbolic planners and blackbox sam- plers via optimistic adaptive planning,

Reference 53

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source=pdf_text observed=2026-08-01T02:37:40.415029Z digest=sha256:c77df82d69ab62dbe701c460b5a76ff89c996c4173fe4d1ed4d18c9d5fb114e9

Observation 5c6ad1d1-2406-47b8-852d-7a722d89c44b · outbound

This paper cites Long-horizon manipulation of unknown objects via task and motion planning with estimated affordances,.

Decompose and Reorganize: Planning with Primitives and Visuomotor Policies Learned from Demonstrations Long-horizon manipulation of unknown objects via task and motion planning with estimated affordances,

Reference 54

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source=pdf_text observed=2026-08-01T02:37:40.418572Z digest=sha256:bbf86358a1230d46e46f6de8f800e9f735e532c06e765564f3418df39c2384b0

Observation b09a6eb7-c39a-4c29-88b3-1af09662f427 · outbound

This paper cites Unseen object instance segmentation for robotic environments,.

Decompose and Reorganize: Planning with Primitives and Visuomotor Policies Learned from Demonstrations Unseen object instance segmentation for robotic environments,

Reference 55

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source=pdf_text observed=2026-08-01T02:37:40.421969Z digest=sha256:91d3dcc5c7e5ba7562fa278082da6095507b41aa00b68384f4e77b7f4d1671e5

Observation 76ef71e1-5b69-42b0-9556-b32066003309 · outbound

This paper cites High precision grasp pose detection in dense clutter,.

Decompose and Reorganize: Planning with Primitives and Visuomotor Policies Learned from Demonstrations High precision grasp pose detection in dense clutter,

Reference 56

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source=pdf_text observed=2026-08-01T02:37:40.425581Z digest=sha256:e288f1b59850aec24d35c4d36e9e38a6c4033b9eb07ca1cd93b409e7ca40f66a

Observation c20a07d2-2625-4717-93dd-671388dc16f6 · outbound

This paper cites 6-DoF Graspnet: Varia- tional grasp generation for object manipulation,.

Decompose and Reorganize: Planning with Primitives and Visuomotor Policies Learned from Demonstrations 6-DoF Graspnet: Varia- tional grasp generation for object manipulation,

Reference 57

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source=pdf_text observed=2026-08-01T02:37:40.429308Z digest=sha256:a03c52858236592c3313bfafcd5d09fbc1aa32500ebaea757da55965a27b7691

Observation adfac66a-5bc0-4af2-93bf-f87581aa1a60 · outbound

This paper cites Gen6D: Generalizable model-free 6-DoF object pose estimation from rgb images,.

Decompose and Reorganize: Planning with Primitives and Visuomotor Policies Learned from Demonstrations Gen6D: Generalizable model-free 6-DoF object pose estimation from rgb images,

Reference 58

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source=pdf_text observed=2026-08-01T02:37:40.432935Z digest=sha256:1ce035ddb40ffac11dcc9e277b196e2056618d107ab7f7b26883e5a1b1bf82c2

Observation 2b58c6fe-a1c6-4f1b-b5f1-575c8376a88f · outbound

This paper cites POPE: 6- DoF promptable pose estimation of any object in any scene with one reference,.

Decompose and Reorganize: Planning with Primitives and Visuomotor Policies Learned from Demonstrations POPE: 6- DoF promptable pose estimation of any object in any scene with one reference,

Reference 59

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source=pdf_text observed=2026-08-01T02:37:40.436308Z digest=sha256:f2ef99fadf169f37871ea3350e06c303842fc493a4494412451c0eb05b263e71

Observation 9f580b1c-f9ee-4ffa-a810-aa808b6f082d · outbound

This paper cites FoundationPose: Unified 6D pose estimation and tracking of novel objects,.

Decompose and Reorganize: Planning with Primitives and Visuomotor Policies Learned from Demonstrations FoundationPose: Unified 6D pose estimation and tracking of novel objects,

Reference 60

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source=pdf_text observed=2026-08-01T02:37:40.439964Z digest=sha256:51bed07f40a9b631b344bfdbc8b48eaa665ea8c64f2c2e0f03e8d695f9faa279

Observation 15c826f5-f113-4bab-b14f-ccb09b1e291a · outbound

This paper cites Unified task and motion planning us- ing object-centric abstractions of motion constraints,.

Decompose and Reorganize: Planning with Primitives and Visuomotor Policies Learned from Demonstrations Unified task and motion planning us- ing object-centric abstractions of motion constraints,

Reference 61

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source=pdf_text observed=2026-08-01T02:37:40.443434Z digest=sha256:a7228945120d21dbca57325983f4c85b24653632343bf172ed1d3e0130e5833b

Observation 1b091d9e-f8d1-4f3c-93ab-80617e7b2513 · outbound

This paper cites Grounded SAM: Assembling Open-World Models for Diverse Visual Tasks.

Decompose and Reorganize: Planning with Primitives and Visuomotor Policies Learned from Demonstrations Grounded SAM: Assembling Open-World Models for Diverse Visual Tasks

Reference 62

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Observation eafb39be-3e5a-41fb-9cd3-88c3cfb4800e · outbound

This paper cites Xmem: Long-term video object segmentation with an atkinson-shiffrin memory model,.

Decompose and Reorganize: Planning with Primitives and Visuomotor Policies Learned from Demonstrations Xmem: Long-term video object segmentation with an atkinson-shiffrin memory model,

Reference 63

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source=pdf_text observed=2026-08-01T02:37:40.450469Z digest=sha256:f34df754a8e6583e948426ba527f067b308245e7e2cc052e7707235f08c3c32c

Observation 269ef48a-8081-4850-93e2-f59b4cc16110 · outbound

This paper cites D (r, o) grasp: A unified representation of robot and object interaction for cross-embodiment dexterous grasping,.

Decompose and Reorganize: Planning with Primitives and Visuomotor Policies Learned from Demonstrations D (r, o) grasp: A unified representation of robot and object interaction for cross-embodiment dexterous grasping,

Reference 64

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source=pdf_text observed=2026-08-01T02:37:40.453879Z digest=sha256:e3cddf7e6ecd1f734861d0b2d2b1f539df046ba866a222296b156b9e26b4e400

Observation 9c895f74-34b3-4853-bdc7-1372d9aa8689 · outbound

This paper cites Vector Neurons: A general framework for SO(3)-equivariant networks,.

Decompose and Reorganize: Planning with Primitives and Visuomotor Policies Learned from Demonstrations Vector Neurons: A general framework for SO(3)-equivariant networks,

Reference 65

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source=pdf_text observed=2026-08-01T02:37:40.457292Z digest=sha256:5202372654dc252a0362d9bad289932c070ffe8097c025c088e2f99374b88498

Observation 924da9aa-8110-4181-9df4-2f7dcabebb62 · outbound

This paper cites Bert: Pre-training of deep bidirectional transformers for language understanding,.

Decompose and Reorganize: Planning with Primitives and Visuomotor Policies Learned from Demonstrations Bert: Pre-training of deep bidirectional transformers for language understanding,

Reference 66

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source=pdf_text observed=2026-08-01T02:37:40.460895Z digest=sha256:26d144fb39dc672ec09ec4e7a63e14892e51fbfb50258f4db68556d07d640aa8

Observation 97dcc459-96c5-4860-b193-5c841b80fc39 · outbound

This paper cites EFEM: Equivariant neural field expectation maximization for 3D object seg- mentation without scene supervision,.

Decompose and Reorganize: Planning with Primitives and Visuomotor Policies Learned from Demonstrations EFEM: Equivariant neural field expectation maximization for 3D object seg- mentation without scene supervision,

Reference 67

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Observation ff2af30c-6521-4f3c-b60d-dda088c9d601 · outbound

This paper cites Denoising Diffusion Probabilistic Models,.

Decompose and Reorganize: Planning with Primitives and Visuomotor Policies Learned from Demonstrations Denoising Diffusion Probabilistic Models,

Reference 68

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source=pdf_text observed=2026-08-01T02:37:40.467989Z digest=sha256:51ebc6be3840e0b125eb338db250e2499b5ccb7d50692430576da39fab959eca

Observation 5d62c15b-0c15-468f-9a23-50d5b0663388 · outbound

This paper cites Should ebms model the energy or the score?.

Decompose and Reorganize: Planning with Primitives and Visuomotor Policies Learned from Demonstrations Should ebms model the energy or the score?

Reference 69

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source=pdf_text observed=2026-08-01T02:37:40.471452Z digest=sha256:51616aeda3904a858e87ce83af0ebb8003d8d47dd62f973d8fd00006040f4564

Observation a24e1dc9-b095-4db9-b90c-c2647753c0d8 · outbound

This paper cites Generative modeling by estimating gradients of the data distribution,.

Decompose and Reorganize: Planning with Primitives and Visuomotor Policies Learned from Demonstrations Generative modeling by estimating gradients of the data distribution,

Reference 70

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source=pdf_text observed=2026-08-01T02:37:40.475330Z digest=sha256:9c673fe5d28725161d9d08e3ff502da38e9adc7acbd0b0936738939c5b20dd30

Observation f4182f0c-1c22-4f15-ba4f-b2afe2257994 · outbound

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

Decompose and Reorganize: Planning with Primitives and Visuomotor Policies Learned from Demonstrations Score-Based Generative Modeling through Stochastic Differential Equations,

Reference 71

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source=pdf_text observed=2026-08-01T02:37:40.478927Z digest=sha256:a3237b8f92edbc3adfd9c657019da8d75813e5364f0248e7d13b35c2dd3706cf

Observation b3ebbf26-6b20-4bd7-addb-9f8d85dfd5af · outbound

This paper cites Coast: Constraints and streams for task and motion planning,.

Decompose and Reorganize: Planning with Primitives and Visuomotor Policies Learned from Demonstrations Coast: Constraints and streams for task and motion planning,

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source=pdf_text observed=2026-08-01T02:37:40.482253Z digest=sha256:6f797eeed7edde39417091cc150b51bf24956e2939e852decfa1912b4b06e43c

Observation 29e6ef59-67ec-4df3-8d16-9ee9d7789294 · outbound

This paper cites Schedulestream: Temporal planning with samplers for gpu-accelerated multi-arm task and motion planning & scheduling,.

Decompose and Reorganize: Planning with Primitives and Visuomotor Policies Learned from Demonstrations Schedulestream: Temporal planning with samplers for gpu-accelerated multi-arm task and motion planning & scheduling,

Reference 73

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source=pdf_text observed=2026-08-01T02:37:40.485624Z digest=sha256:7d376a326b77911d8104a7e425c2a9c66fab3214e659db46298a8f61df96d30b

Observation 30f3ca2b-317e-4bcd-8634-1170084c2de4 · outbound

This paper cites Gpu-accelerated incremental euclidean distance transform for online motion planning of mobile robots,.

Decompose and Reorganize: Planning with Primitives and Visuomotor Policies Learned from Demonstrations Gpu-accelerated incremental euclidean distance transform for online motion planning of mobile robots,

Reference 74

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source=pdf_text observed=2026-08-01T02:37:40.489076Z digest=sha256:c041898e5e3a092539d5452b407201918a654896741e09f2b96091631a45c2b3

Observation 62585029-4d08-4908-aecf-988e2a2cdf3f · outbound

This paper cites One demo is worth a thousand trajectories: Action-view augmentation for visuomotor policies,.

Decompose and Reorganize: Planning with Primitives and Visuomotor Policies Learned from Demonstrations One demo is worth a thousand trajectories: Action-view augmentation for visuomotor policies,

Reference 75

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source=pdf_text observed=2026-08-01T02:37:40.492435Z digest=sha256:06ef9631159ae87f70d550870109564ab46ab8c0b657b559b67f9dd756c1188b

Observation 207ae94e-a70c-4236-936c-17f6381b2cf5 · outbound

This paper cites Rail: Reachability-aided imitation learning for safe policy execution,.

Decompose and Reorganize: Planning with Primitives and Visuomotor Policies Learned from Demonstrations Rail: Reachability-aided imitation learning for safe policy execution,

Reference 76

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source=pdf_text observed=2026-08-01T02:37:40.496485Z digest=sha256:eb014ffc0d592103aa33f788d1cfb58efa2088982a2091a66495e8f11b61d291

Observation ab8279be-35a1-4d5f-a7f0-7df5852daed5 · outbound

This paper cites Libero: Benchmarking knowledge transfer for lifelong robot learning,.

Decompose and Reorganize: Planning with Primitives and Visuomotor Policies Learned from Demonstrations Libero: Benchmarking knowledge transfer for lifelong robot learning,

Reference 77

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source=pdf_text observed=2026-08-01T02:37:40.500015Z digest=sha256:97ccf02e2c5e61480419a2c179df34033a8aaf9398c72ecdf7f82440bfe1fb7a

Observation d9671d57-602c-498c-8b21-dccf90f7198a · outbound

This paper cites Grasp pose detection in point clouds,.

Decompose and Reorganize: Planning with Primitives and Visuomotor Policies Learned from Demonstrations Grasp pose detection in point clouds,

Reference 78

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source=pdf_text observed=2026-08-01T02:37:40.503650Z digest=sha256:c2057c175106ee42244956fb54a213c6afb47dc45af7054ed2392958a26f3692

Observation 6b375058-a36f-4095-b64a-9a3d3e67d086 · outbound

This paper cites M2t2: Multi-task masked transformer for object-centric pick and place,.

Decompose and Reorganize: Planning with Primitives and Visuomotor Policies Learned from Demonstrations M2t2: Multi-task masked transformer for object-centric pick and place,

Reference 79

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source=pdf_text observed=2026-08-01T02:37:40.507187Z digest=sha256:81dc3e744a9b8a03147dd42f0724088b9de3bd780d1fa3288e85fefe1c102426

Observation 03f69504-bda6-4e27-998f-40caa8208926 · outbound

This paper cites TiPToP: A Modular Open-Vocabulary Robot Manipulation System That Plans.

Decompose and Reorganize: Planning with Primitives and Visuomotor Policies Learned from Demonstrations TiPToP: A Modular Open-Vocabulary Robot Manipulation System That Plans

Reference 80

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source=pdf_text observed=2026-08-01T02:37:40.510681Z digest=sha256:86fe93eddbeaf45d64234ced70acb204c3e80f201580dea0dadf8a90e9600173

Observation b456a7c7-e8a4-473a-8101-b6b909c441c8 · outbound

This paper cites Se (3)-equivariant diffusion policy in spherical fourier space,.

Decompose and Reorganize: Planning with Primitives and Visuomotor Policies Learned from Demonstrations Se (3)-equivariant diffusion policy in spherical fourier space,

Reference 81

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source=pdf_text observed=2026-08-01T02:37:40.514383Z digest=sha256:23e173468eccbb187ca58dbcd92d5f94aa8cc3525d273b45ae7e37cf74e0b499

Observation a62fb0c3-7e22-42c6-87cc-2525a140da0f · outbound

This paper cites Translating Natural Language to Planning Goals with Large-Language Models.

Decompose and Reorganize: Planning with Primitives and Visuomotor Policies Learned from Demonstrations Translating Natural Language to Planning Goals with Large-Language Models

Reference 82

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source=pdf_text observed=2026-08-01T02:37:40.517728Z digest=sha256:cc544186b685762fae00b72acff3cae04509ca4deeb7b4c8fd2932a9e116ea08

Observation c03aa334-8e33-4bee-932c-389c29c3c21e · outbound

This paper cites Integrated Task and Motion Planning,.

Decompose and Reorganize: Planning with Primitives and Visuomotor Policies Learned from Demonstrations Integrated Task and Motion Planning,

Reference 83

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source=pdf_text observed=2026-08-01T02:37:40.521671Z digest=sha256:d874c5822ec6201af46361ded686ee79bf765683252fd01140c0d9054a094dc1

Observation 1bbf1352-a8cc-4a34-a942-521795306438 · outbound

This paper cites Differentiable gpu-parallelized task and motion planning,.

Decompose and Reorganize: Planning with Primitives and Visuomotor Policies Learned from Demonstrations Differentiable gpu-parallelized task and motion planning,

Reference 84

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source=pdf_text observed=2026-08-01T02:37:40.525754Z digest=sha256:b820a2cc2511d8d516c30c3b4380110beb5f2fe5744559a8a0d6bdc8fcd86a8a

Observation e4b2243f-7dd3-4697-85b6-4ca03a5dc9fd · outbound

This paper cites Online replanning in belief space for partially observable task and motion problems,.

Decompose and Reorganize: Planning with Primitives and Visuomotor Policies Learned from Demonstrations Online replanning in belief space for partially observable task and motion problems,

Reference 85

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source=pdf_text observed=2026-08-01T02:37:40.529413Z digest=sha256:43d8ff5d8a6c9a7f38b036a8592e9edebc44f22c05cae8ed223d93f6283e557a

Observation 6eb5bfb1-86b0-4139-8935-23224308a7c7 · outbound

This paper cites Foundationstereo: Zero-shot stereo matching,.

Decompose and Reorganize: Planning with Primitives and Visuomotor Policies Learned from Demonstrations Foundationstereo: Zero-shot stereo matching,

Reference 86

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source=pdf_text observed=2026-08-01T02:37:40.533185Z digest=sha256:4cdb6122c255cbe906cd76afcda331b3be71d672f37866b46554857bedab70ae

Observation 4ed1cf24-39b7-499d-8b15-4d4a414ca655 · outbound

This paper cites Morphing and sampling network for dense point cloud completion,.

Decompose and Reorganize: Planning with Primitives and Visuomotor Policies Learned from Demonstrations Morphing and sampling network for dense point cloud completion,

Reference 87

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source=pdf_text observed=2026-08-01T02:37:40.536770Z digest=sha256:fe5aad22d8a53a53114137d30ef3b44ecb4be5739399295eec8e4f78760c3583

Observation 6546c734-d9d7-42c9-ae3f-c0ce0025ff19 · outbound

This paper cites Equivariant diffusion policy,.

Decompose and Reorganize: Planning with Primitives and Visuomotor Policies Learned from Demonstrations Equivariant diffusion policy,

Reference 88

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source=pdf_text observed=2026-08-01T02:37:40.540160Z digest=sha256:32e4926dd29e5231839cb40fe53907ed2724519846afc8228c92f5a39441e8ed

Observation 1ea4d98e-8174-4a2b-8ca8-8ea6c11c0d4e · outbound

This paper cites A success termination is defined as the cup being fully inserted into the sleeve.

Decompose and Reorganize: Planning with Primitives and Visuomotor Policies Learned from Demonstrations A success termination is defined as the cup being fully inserted into the sleeve

Reference 89

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source=pdf_text observed=2026-08-01T02:37:40.543901Z digest=sha256:265d9dbecb21056bd68746be8bec1976b39ccaefd42163d2ae794391251808b5

Observation 6fdfdde4-7f44-4ae5-b373-34a10f0c85d2 · outbound

This paper cites The right gripper then closes the lid, which is finally pressed by both grippers.

Decompose and Reorganize: Planning with Primitives and Visuomotor Policies Learned from Demonstrations The right gripper then closes the lid, which is finally pressed by both grippers

Reference 90

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source=pdf_text observed=2026-08-01T02:37:40.547962Z digest=sha256:985bd82da5ea8bf59573e810cd1a00638bde006db3748a9a1ae8fe45a5592aa1

Observation 1f6d6b4b-c794-4475-980b-28c113109ac1 · outbound

This paper cites A success termination is defined as the tape being firmly grasped by the receiving left gripper.

Decompose and Reorganize: Planning with Primitives and Visuomotor Policies Learned from Demonstrations A success termination is defined as the tape being firmly grasped by the receiving left gripper

Reference 91

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source=pdf_text observed=2026-08-01T02:37:40.551517Z digest=sha256:fcbeb383476e6688918d9dbb6a6ba69caed064a0b678f571a45ca13d6e9cd515

Observation 25c8cb7c-8e60-4509-a345-ad4d0c3432e0 · outbound

This paper cites The left robot picks up the tripod, the right robot picks up the needle, and the robots perform bimanual threading.

Decompose and Reorganize: Planning with Primitives and Visuomotor Policies Learned from Demonstrations The left robot picks up the tripod, the right robot picks up the needle, and the robots perform bimanual threading

Reference 92

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source=pdf_text observed=2026-08-01T02:37:40.555320Z digest=sha256:220140b1ec83f0a6ce271e52c02fdbd6c6062d3fb3475b73b865deead88b9856

Pith citing papers

No inbound Pith citation observations are available.