Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-07T05:59:30.182455Z
Paper Citation Record · LEDGER
As of 7 August 2026, this Paper Citation Record lists 63 of 63 outbound references and 2 inbound Pith citation observations for arXiv:2506.06535.
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 2026-08-07T05:59:30.182455Z
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-07-31T06:18:55.243330Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-17T20:00:10.735031Z
63 of 63 outbound references displayed
External citation measurements
No source-named external measurement is stored.
Observation 95d3747e-a435-4af7-819f-b34f2fe1c027 · outbound
MapleGrasp: Mask-guided Feature Pooling for Language-driven Efficient Robotic Grasping End-to-end train- able deep neural network for robotic grasp detection and semantic segmentation from rgb
Reference 1
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MapleGrasp: Mask-guided Feature Pooling for Language-driven Efficient Robotic Grasping Hifi-cs: Towards open vocabulary visual grounding for robotic grasping using vision-language mod- els, 2024
Reference 2
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MapleGrasp: Mask-guided Feature Pooling for Language-driven Efficient Robotic Grasping RT-2: Vision-language-action models transfer web knowledge to robotic control
Reference 3
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MapleGrasp: Mask-guided Feature Pooling for Language-driven Efficient Robotic Grasping Open X-Embodiment: Robotic Learning Datasets and RT-X Models
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MapleGrasp: Mask-guided Feature Pooling for Language-driven Efficient Robotic Grasping Trust the PRoc3s: Solving long-horizon robotics problems with LLMs and constraint satisfaction
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MapleGrasp: Mask-guided Feature Pooling for Language-driven Efficient Robotic Grasping Smith, Hannaneh Hajishirzi, Ross Girshick, Ali Farhadi, and Aniruddha Kembhavi
Reference 6
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MapleGrasp: Mask-guided Feature Pooling for Language-driven Efficient Robotic Grasping Jacquard: A large scale dataset for robotic grasp detection
Reference 7
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MapleGrasp: Mask-guided Feature Pooling for Language-driven Efficient Robotic Grasping GraspMolmo: Generalizable Task-Oriented Grasping via Large-Scale Synthetic Data Generation
Reference 8
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Observation c4544b63-71b8-4fb6-a098-b045da646004 · outbound
MapleGrasp: Mask-guided Feature Pooling for Language-driven Efficient Robotic Grasping Acronym: A large-scale grasp dataset based on simulation
Reference 9
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Observation 0058bcd9-a88b-4582-98c9-09c158d40814 · outbound
MapleGrasp: Mask-guided Feature Pooling for Language-driven Efficient Robotic Grasping GraspNet-1Billion: A large-scale benchmark for general object grasping
Reference 10
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Observation 80e38701-26c3-4df1-8cc3-b79c95fa4ef2 · outbound
MapleGrasp: Mask-guided Feature Pooling for Language-driven Efficient Robotic Grasping Physically grounded vision-language models for robotic manipulation
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MapleGrasp: Mask-guided Feature Pooling for Language-driven Efficient Robotic Grasping Rvt2: Learning precise manipulation from few demonstrations
Reference 12
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MapleGrasp: Mask-guided Feature Pooling for Language-driven Efficient Robotic Grasping Language-grounded dy- namic scene graphs for interactive object search with mobile manipulation
Reference 13
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MapleGrasp: Mask-guided Feature Pooling for Language-driven Efficient Robotic Grasping Inner monologue: Embodied reason- ing through planning with language models
Reference 14
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Observation e3a2a822-747f-408d-88b2-8dec101af597 · outbound
MapleGrasp: Mask-guided Feature Pooling for Language-driven Efficient Robotic Grasping Effi- cient grasping from rgbd images: Learning using a new rect- angle representation
Reference 15
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Observation ac6541b7-d66c-424f-9970-9ab555c89a33 · outbound
MapleGrasp: Mask-guided Feature Pooling for Language-driven Efficient Robotic Grasping Prismatic vlms: Investigating the design space of visually-conditioned language models
Reference 16
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MapleGrasp: Mask-guided Feature Pooling for Language-driven Efficient Robotic Grasping OpenVLA: An Open-Source Vision-Language-Action Model
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MapleGrasp: Mask-guided Feature Pooling for Language-driven Efficient Robotic Grasping Fine-Tuning Vision-Language-Action Models: Optimizing Speed and Success
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MapleGrasp: Mask-guided Feature Pooling for Language-driven Efficient Robotic Grasping Segment anything
Reference 19
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Observation 6a96d6ae-0ffe-4182-b7f6-2a1a65e858ec · outbound
MapleGrasp: Mask-guided Feature Pooling for Language-driven Efficient Robotic Grasping Antipodal robotic grasping using generative residual convolutional neu- ral network
Reference 20
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MapleGrasp: Mask-guided Feature Pooling for Language-driven Efficient Robotic Grasping Ovgnet: A unified visual-linguistic framework for open-vocabulary robotic grasping
Reference 21
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MapleGrasp: Mask-guided Feature Pooling for Language-driven Efficient Robotic Grasping Ovgnet: A unified visual-linguistic framework for open-vocabulary robotic grasping
Reference 22
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Observation 31909571-58c9-41a6-b582-3ab879eff332 · outbound
MapleGrasp: Mask-guided Feature Pooling for Language-driven Efficient Robotic Grasping Vision-language foun- dation models as effective robot imitators
Reference 23
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Observation 15acdb14-72b2-4a3b-9598-749606a6de8d · outbound
MapleGrasp: Mask-guided Feature Pooling for Language-driven Efficient Robotic Grasping Unresolved cited work
Reference 24
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Observation 0c3965e4-eaeb-458b-8cd7-3bf30b0f60a7 · outbound
MapleGrasp: Mask-guided Feature Pooling for Language-driven Efficient Robotic Grasping OK-Robot: What Really Matters in Integrating Open-Knowledge Models for Robotics
Reference 25
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Observation 4e0eb91f-eaf0-42a7-ac46-1c547ca68b6e · outbound
MapleGrasp: Mask-guided Feature Pooling for Language-driven Efficient Robotic Grasping Grounding dino: Marry- ing dino with grounded pre-training for open-set object de- tection
Reference 26
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Observation 04bbb5e5-bf10-45f8-b658-e3ea8faf660c · outbound
MapleGrasp: Mask-guided Feature Pooling for Language-driven Efficient Robotic Grasping Gao, Xi Vin- cent Wang, and Lihui Wang
Reference 27
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MapleGrasp: Mask-guided Feature Pooling for Language-driven Efficient Robotic Grasping Deepseek-vl: Towards real-world vision- language understanding, 2024
Reference 28
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MapleGrasp: Mask-guided Feature Pooling for Language-driven Efficient Robotic Grasping Hybrid physical metric for 6-dof grasp pose detection
Reference 29
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MapleGrasp: Mask-guided Feature Pooling for Language-driven Efficient Robotic Grasping Vl-grasp: a 6-dof interactive grasp pol- icy for language-oriented objects in cluttered indoor scenes
Reference 30
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MapleGrasp: Mask-guided Feature Pooling for Language-driven Efficient Robotic Grasping GraspMamba: A Mamba-based Language-driven Grasp Detection Framework with Hierarchical Feature Learning
Reference 31
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MapleGrasp: Mask-guided Feature Pooling for Language-driven Efficient Robotic Grasping Lightweight language-driven grasp detection using conditional consis- tency model
Reference 32
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MapleGrasp: Mask-guided Feature Pooling for Language-driven Efficient Robotic Grasping Language-driven 6-dof grasp detection using negative prompt guidance
Reference 33
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MapleGrasp: Mask-guided Feature Pooling for Language-driven Efficient Robotic Grasping GraspSAM: When Segment Anything Model Meets Grasp Detection
Reference 34
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MapleGrasp: Mask-guided Feature Pooling for Language-driven Efficient Robotic Grasping 3D-MVP: 3D Multiview Pretraining for Robotic Manipulation
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MapleGrasp: Mask-guided Feature Pooling for Language-driven Efficient Robotic Grasping Sayplan: Ground- ing large language models using 3d scene graphs for scalable task planning
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MapleGrasp: Mask-guided Feature Pooling for Language-driven Efficient Robotic Grasping SAM 2: Segment anything in images and videos
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MapleGrasp: Mask-guided Feature Pooling for Language-driven Efficient Robotic Grasping Sadler, Wei-Lun Chao, and Yu Su
Reference 38
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MapleGrasp: Mask-guided Feature Pooling for Language-driven Efficient Robotic Grasping Grasping in the wild: Learning 6dof closed- loop grasping from low-cost demonstrations
Reference 39
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MapleGrasp: Mask-guided Feature Pooling for Language-driven Efficient Robotic Grasping Contact-graspnet: Efficient 6-dof grasp gen- eration in cluttered scenes
Reference 40
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MapleGrasp: Mask-guided Feature Pooling for Language-driven Efficient Robotic Grasping Foundationgrasp: Generalizable task-oriented grasping with foundation models
Reference 41
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Reference 42
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Reference 43
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Reference 45
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Reference 49
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Reference 59
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Reference 62
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Reference 63
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