Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-01T02:37:40.555320Z
Paper Citation Record · LEDGER
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.
Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-01T02:37:40.555320Z
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
92 of 92 outbound references displayed
External citation measurements
No source-named external measurement is stored.
Observation 64565c1b-d447-41a5-abb9-f234511a2a17 · outbound
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
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
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
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
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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Observation 9b8dcb17-c92f-455c-8682-5b34f107838f · outbound
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
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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Observation d017bd0c-2ec9-49ae-9fea-0003da6b825c · outbound
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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Observation a643d6b1-ac76-42c8-ae92-bb0ee2ece03a · outbound
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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Observation 7947aac2-6454-44f8-803c-0685a1abc89d · outbound
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
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
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
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
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
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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Observation e0701080-93b1-438f-8558-1c4ba3f0bfb6 · outbound
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
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
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
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
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
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
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
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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Observation e0e47f1a-89d9-4959-bcec-7d9fe0026d1a · outbound
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
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
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
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
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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Observation 535aa422-603f-484b-a219-3e372952089a · outbound
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
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
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
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
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
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
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
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
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
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
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
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
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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Observation 33960a3a-78d7-4338-8abd-dec90bdc54c2 · outbound
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
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
Decompose and Reorganize: Planning with Primitives and Visuomotor Policies Learned from Demonstrations Learning Reusable Manipulation Strategies,
Reference 44
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Observation 3918752e-a35c-49af-9ef7-54658d79abb7 · outbound
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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Observation adde28e8-1050-4c71-a206-2412e5bbc3b6 · outbound
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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Observation 68ab13e8-188d-4e2a-85f2-dbe70048d8c1 · outbound
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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Observation 589c10cb-66c8-4c3c-bf93-bb634f4f220f · outbound
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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Observation 4b1312bb-3b8e-4240-882f-54c8e5ebf083 · outbound
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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Observation d385d11d-bf74-45f3-ad09-54579df0b9d9 · outbound
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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Observation 133c0150-dea3-4efb-b808-da3c3efe2ead · outbound
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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Observation 74d7af55-8bfb-4dfd-9796-57146ba4a650 · outbound
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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Observation 47d52902-7712-47e7-a8c2-fc9099a04880 · outbound
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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Observation 5c6ad1d1-2406-47b8-852d-7a722d89c44b · outbound
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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Observation b09a6eb7-c39a-4c29-88b3-1af09662f427 · outbound
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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Observation 76ef71e1-5b69-42b0-9556-b32066003309 · outbound
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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Observation c20a07d2-2625-4717-93dd-671388dc16f6 · outbound
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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Observation adfac66a-5bc0-4af2-93bf-f87581aa1a60 · outbound
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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Observation 2b58c6fe-a1c6-4f1b-b5f1-575c8376a88f · outbound
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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Observation 9f580b1c-f9ee-4ffa-a810-aa808b6f082d · outbound
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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Observation 15c826f5-f113-4bab-b14f-ccb09b1e291a · outbound
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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Observation 1b091d9e-f8d1-4f3c-93ab-80617e7b2513 · outbound
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
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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Observation 269ef48a-8081-4850-93e2-f59b4cc16110 · outbound
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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Observation 9c895f74-34b3-4853-bdc7-1372d9aa8689 · outbound
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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Observation 924da9aa-8110-4181-9df4-2f7dcabebb62 · outbound
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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Observation 97dcc459-96c5-4860-b193-5c841b80fc39 · outbound
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
Decompose and Reorganize: Planning with Primitives and Visuomotor Policies Learned from Demonstrations Denoising Diffusion Probabilistic Models,
Reference 68
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Observation 5d62c15b-0c15-468f-9a23-50d5b0663388 · outbound
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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Observation a24e1dc9-b095-4db9-b90c-c2647753c0d8 · outbound
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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Observation f4182f0c-1c22-4f15-ba4f-b2afe2257994 · outbound
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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Observation b3ebbf26-6b20-4bd7-addb-9f8d85dfd5af · outbound
Decompose and Reorganize: Planning with Primitives and Visuomotor Policies Learned from Demonstrations Coast: Constraints and streams for task and motion planning,
Reference 72
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Observation 29e6ef59-67ec-4df3-8d16-9ee9d7789294 · outbound
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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Observation 30f3ca2b-317e-4bcd-8634-1170084c2de4 · outbound
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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Observation 62585029-4d08-4908-aecf-988e2a2cdf3f · outbound
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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Observation 207ae94e-a70c-4236-936c-17f6381b2cf5 · outbound
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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Observation ab8279be-35a1-4d5f-a7f0-7df5852daed5 · outbound
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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Observation d9671d57-602c-498c-8b21-dccf90f7198a · outbound
Decompose and Reorganize: Planning with Primitives and Visuomotor Policies Learned from Demonstrations Grasp pose detection in point clouds,
Reference 78
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Observation 6b375058-a36f-4095-b64a-9a3d3e67d086 · outbound
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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Observation 03f69504-bda6-4e27-998f-40caa8208926 · outbound
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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Observation b456a7c7-e8a4-473a-8101-b6b909c441c8 · outbound
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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Observation a62fb0c3-7e22-42c6-87cc-2525a140da0f · outbound
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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Observation c03aa334-8e33-4bee-932c-389c29c3c21e · outbound
Decompose and Reorganize: Planning with Primitives and Visuomotor Policies Learned from Demonstrations Integrated Task and Motion Planning,
Reference 83
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Observation 1bbf1352-a8cc-4a34-a942-521795306438 · outbound
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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Observation e4b2243f-7dd3-4697-85b6-4ca03a5dc9fd · outbound
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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Observation 6eb5bfb1-86b0-4139-8935-23224308a7c7 · outbound
Decompose and Reorganize: Planning with Primitives and Visuomotor Policies Learned from Demonstrations Foundationstereo: Zero-shot stereo matching,
Reference 86
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Observation 4ed1cf24-39b7-499d-8b15-4d4a414ca655 · outbound
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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Observation 6546c734-d9d7-42c9-ae3f-c0ce0025ff19 · outbound
Decompose and Reorganize: Planning with Primitives and Visuomotor Policies Learned from Demonstrations Equivariant diffusion policy,
Reference 88
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Observation 1ea4d98e-8174-4a2b-8ca8-8ea6c11c0d4e · outbound
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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Observation 6fdfdde4-7f44-4ae5-b373-34a10f0c85d2 · outbound
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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Observation 1f6d6b4b-c794-4475-980b-28c113109ac1 · outbound
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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Observation 25c8cb7c-8e60-4509-a345-ad4d0c3432e0 · outbound
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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No inbound Pith citation observations are available.