VLAff: Vision-Language-Affordance Model for Unified Actionable Affordances | Paper Citation Record

Observation 4afcadd5-0916-4933-95db-b78d56ec6edd · outbound

This paper cites Affordance detection of tool parts from geometric features,.

VLAff: Vision-Language-Affordance Model for Unified Actionable Affordances Affordance detection of tool parts from geometric features,

Reference 1

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Observation 737c059f-3284-4117-bad3-93d266628e98 · outbound

This paper cites Learning affordance grounding from exocentric im- ages,.

VLAff: Vision-Language-Affordance Model for Unified Actionable Affordances Learning affordance grounding from exocentric im- ages,

Reference 2

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Observation 45ecd236-96a7-471d-9a0b-a4736986750f · outbound

This paper cites Object-based affordances detection with convo- lutional neural networks and dense conditional random fields,.

VLAff: Vision-Language-Affordance Model for Unified Actionable Affordances Object-based affordances detection with convo- lutional neural networks and dense conditional random fields,

Reference 3

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Observation 42711c5e-a75c-4848-8349-a382a2261f98 · outbound

This paper cites Learning to act properly: Predicting and explain- ing affordances from images,.

VLAff: Vision-Language-Affordance Model for Unified Actionable Affordances Learning to act properly: Predicting and explain- ing affordances from images,

Reference 4

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Observation a1e962bb-a85c-4fce-9409-fcff03c19749 · outbound

This paper cites GLOVER++: Unleashing the Potential of Affordance Learning from Human Behaviors for Robotic Manipulation.

VLAff: Vision-Language-Affordance Model for Unified Actionable Affordances GLOVER++: Unleashing the Potential of Affordance Learning from Human Behaviors for Robotic Manipulation

Reference 5

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Observation 62e92869-c436-4582-b2e1-320c4eea03ca · outbound

This paper cites H2o: Two hands manipulating objects for first person interaction recognition,.

VLAff: Vision-Language-Affordance Model for Unified Actionable Affordances H2o: Two hands manipulating objects for first person interaction recognition,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T18:01:17.895149Z

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Observation 6f046d92-7583-46e3-b762-7ef55f82d38e · outbound

This paper cites Hoi4d: A 4d egocentric dataset for category-level human-object interaction,.

VLAff: Vision-Language-Affordance Model for Unified Actionable Affordances Hoi4d: A 4d egocentric dataset for category-level human-object interaction,

Reference 7

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Observation 415a5eaf-460c-4d5f-947b-2c03f368e661 · outbound

This paper cites Scaling egocentric vision: The epic-kitchens dataset,.

VLAff: Vision-Language-Affordance Model for Unified Actionable Affordances Scaling egocentric vision: The epic-kitchens dataset,

Reference 8

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Observation 25a8e8db-9fa3-4db0-8dc0-8cc1165cfd06 · outbound

This paper cites HD-EPIC: A Highly-Detailed Egocentric Video Dataset.

VLAff: Vision-Language-Affordance Model for Unified Actionable Affordances HD-EPIC: A Highly-Detailed Egocentric Video Dataset

Reference 9

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Observation 03f5bd15-b973-486e-b67a-30ca984cf170 · outbound

This paper cites Ego4d: Around the world in 3,000 hours of egocentric video,.

VLAff: Vision-Language-Affordance Model for Unified Actionable Affordances Ego4d: Around the world in 3,000 hours of egocentric video,

Reference 10

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Observation 256cdff0-b054-428c-b8fe-391d72767447 · outbound

This paper cites Ego-Exo4D: Understanding Skilled Human Activity from First- and Third-Person Perspectives.

VLAff: Vision-Language-Affordance Model for Unified Actionable Affordances Ego-Exo4D: Understanding Skilled Human Activity from First- and Third-Person Perspectives

Reference 11

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Observation 6fb84ae5-9966-4d9b-9ea9-393e4f6c26d4 · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

VLAff: Vision-Language-Affordance Model for Unified Actionable Affordances LLaMA: Open and Efficient Foundation Language Models

Reference 12

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Observation 5c31d479-3971-455c-ac56-ae9a4f27d3b1 · outbound

This paper cites GPT-4 Technical Report.

VLAff: Vision-Language-Affordance Model for Unified Actionable Affordances GPT-4 Technical Report

Reference 13

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Observation 1d069619-2cca-4b88-8899-aa069c6b5363 · outbound

This paper cites Efficient training of artificial neural networks for autonomous navigation,.

VLAff: Vision-Language-Affordance Model for Unified Actionable Affordances Efficient training of artificial neural networks for autonomous navigation,

Reference 14

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Observation 7453ea79-a5e4-4e0c-aace-a6499a1de816 · outbound

This paper cites VIP: Towards Universal Visual Reward and Representation via Value-Implicit Pre-Training.

VLAff: Vision-Language-Affordance Model for Unified Actionable Affordances VIP: Towards Universal Visual Reward and Representation via Value-Implicit Pre-Training

Reference 15

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Observation 199bafe5-7125-46a3-9edb-066e3711d317 · outbound

This paper cites Masked Visual Pre-training for Motor Control.

VLAff: Vision-Language-Affordance Model for Unified Actionable Affordances Masked Visual Pre-training for Motor Control

Reference 16

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Observation f311e024-7c68-41b7-b879-9780b9ff46ba · outbound

This paper cites R3M: A Universal Visual Representation for Robot Manipulation.

VLAff: Vision-Language-Affordance Model for Unified Actionable Affordances R3M: A Universal Visual Representation for Robot Manipulation

Reference 17

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Observation ecdd8d95-bbfd-49fd-a542-c2319d15605f · outbound

This paper cites an unresolved cited work.

VLAff: Vision-Language-Affordance Model for Unified Actionable Affordances Unresolved cited work

Reference 18

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Observation 7b81d6aa-d3c8-4c2f-b0e0-b2360a35ca36 · outbound

This paper cites Affordances from human videos as a versatile repre- sentation for robotics,.

VLAff: Vision-Language-Affordance Model for Unified Actionable Affordances Affordances from human videos as a versatile repre- sentation for robotics,

Reference 19

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Observation 037045af-ee10-450d-ae62-be357e4848d7 · outbound

This paper cites GLOVER: Generalizable Open-Vocabulary Affordance Reasoning for Task-Oriented Grasping.

VLAff: Vision-Language-Affordance Model for Unified Actionable Affordances GLOVER: Generalizable Open-Vocabulary Affordance Reasoning for Task-Oriented Grasping

Reference 20

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Observation 47592aa3-039a-4610-9769-65ec639969e8 · outbound

This paper cites Dexycb: A benchmark for capturing hand grasping of objects,.

VLAff: Vision-Language-Affordance Model for Unified Actionable Affordances Dexycb: A benchmark for capturing hand grasping of objects,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T18:01:17.712184Z

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Observation a049b818-b85a-429c-a7a2-3a051bbb495e · outbound

This paper cites Videodex: Learning dexterity from internet videos,.

VLAff: Vision-Language-Affordance Model for Unified Actionable Affordances Videodex: Learning dexterity from internet videos,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T18:01:17.697193Z

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Observation 185a3a09-3a05-491c-9e43-c17168325096 · outbound

This paper cites Where are we in the search for an Artificial Visual Cortex for Embodied Intelligence?.

VLAff: Vision-Language-Affordance Model for Unified Actionable Affordances Where are we in the search for an Artificial Visual Cortex for Embodied Intelligence?

Reference 23

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Observation ac586404-0b6b-43e5-8dfe-a93647b520b7 · outbound

This paper cites LIV: Language-Image Representations and Rewards for Robotic Control.

VLAff: Vision-Language-Affordance Model for Unified Actionable Affordances LIV: Language-Image Representations and Rewards for Robotic Control

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-08T18:01:16.609530Z

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Observation 9a3bd658-5224-4d52-b996-4ef86f051fa4 · outbound

This paper cites Hrp: Human affordances for robotic pre- training,.

VLAff: Vision-Language-Affordance Model for Unified Actionable Affordances Hrp: Human affordances for robotic pre- training,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T18:01:17.688991Z

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Observation 1a14e57d-602e-4b17-9612-b82b3bf0bf1c · outbound

This paper cites VidBot: Learning Generalizable 3D Actions from In-the-Wild 2D Human Videos for Zero-Shot Robotic Manipulation.

VLAff: Vision-Language-Affordance Model for Unified Actionable Affordances VidBot: Learning Generalizable 3D Actions from In-the-Wild 2D Human Videos for Zero-Shot Robotic Manipulation

Reference 26

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Observation 28494b12-e07b-4b42-9380-104fbf4387bc · outbound

This paper cites Weakly supervised affordance detection,.

VLAff: Vision-Language-Affordance Model for Unified Actionable Affordances Weakly supervised affordance detection,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T18:01:17.680508Z

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Observation 82283a00-7753-47a8-9c02-746ed0f2ea23 · outbound

This paper cites Locate: Localize and transfer object parts for weakly supervised affordance grounding,.

VLAff: Vision-Language-Affordance Model for Unified Actionable Affordances Locate: Localize and transfer object parts for weakly supervised affordance grounding,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T18:01:17.632639Z

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Observation 302c82a3-ba4f-432a-812d-0595356cd66e · outbound

This paper cites Affordancellm: Grounding affordance from vision language models,.

VLAff: Vision-Language-Affordance Model for Unified Actionable Affordances Affordancellm: Grounding affordance from vision language models,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T18:01:17.530197Z

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Observation 7d9ddb00-788c-4c53-af49-f5898a6771a5 · outbound

This paper cites UAD: Unsupervised Affordance Distillation for Generalization in Robotic Manipulation.

VLAff: Vision-Language-Affordance Model for Unified Actionable Affordances UAD: Unsupervised Affordance Distillation for Generalization in Robotic Manipulation

Reference 30

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Observation f2e4e690-726b-4e55-8dee-6bb6b08e5796 · outbound

This paper cites Arctic: A dataset for dexterous bimanual hand-object manipulation,.

VLAff: Vision-Language-Affordance Model for Unified Actionable Affordances Arctic: A dataset for dexterous bimanual hand-object manipulation,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T18:01:17.517069Z

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Observation 2d0a5f96-7ad8-4499-bec2-947c838f8307 · outbound

This paper cites Contactpose: A dataset of grasps with object contact and hand pose,.

VLAff: Vision-Language-Affordance Model for Unified Actionable Affordances Contactpose: A dataset of grasps with object contact and hand pose,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T18:01:17.508549Z

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Observation 77ba9438-3b92-4fc7-bc55-fdad3caf5c21 · outbound

This paper cites Oakink: A large-scale knowledge repository for understanding hand-object interaction,.

VLAff: Vision-Language-Affordance Model for Unified Actionable Affordances Oakink: A large-scale knowledge repository for understanding hand-object interaction,

Reference 33

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Observation ae0e54f0-4f30-4e0f-bb85-d5738497ec7a · outbound

This paper cites TACO: Benchmarking Generalizable Bimanual Tool-ACtion-Object Understanding.

VLAff: Vision-Language-Affordance Model for Unified Actionable Affordances TACO: Benchmarking Generalizable Bimanual Tool-ACtion-Object Understanding

Reference 34

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Observation a7fa2ba1-2ad6-4042-a8a6-b099d5b6de3b · outbound

This paper cites RT-1: Robotics Transformer for Real-World Control at Scale.

VLAff: Vision-Language-Affordance Model for Unified Actionable Affordances RT-1: Robotics Transformer for Real-World Control at Scale

Reference 35

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Observation f0834910-a2e2-44ea-b176-3987593bb2f3 · outbound

This paper cites RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control.

VLAff: Vision-Language-Affordance Model for Unified Actionable Affordances RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control

Reference 36

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Observation 860b4e08-c7b5-4619-9d48-352a770d01a5 · outbound

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

VLAff: Vision-Language-Affordance Model for Unified Actionable Affordances OpenVLA: An Open-Source Vision-Language-Action Model

Reference 37

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Observation 73ee612d-2cd4-45f6-bc31-a5f081d21bec · outbound

This paper cites Octo: An Open-Source Generalist Robot Policy.

VLAff: Vision-Language-Affordance Model for Unified Actionable Affordances Octo: An Open-Source Generalist Robot Policy

Reference 38

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Observation 740781d5-193d-4326-9eb2-ceca4192ae9f · outbound

This paper cites Open X-Embodiment: Robotic Learning Datasets and RT-X Models.

VLAff: Vision-Language-Affordance Model for Unified Actionable Affordances Open X-Embodiment: Robotic Learning Datasets and RT-X Models

Reference 39

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Observation 4a6f6391-124f-473e-bc9c-ac5037d9c8fd · outbound

This paper cites Droid: A large-scale in-the-wild robot manipu- lation dataset,.

VLAff: Vision-Language-Affordance Model for Unified Actionable Affordances Droid: A large-scale in-the-wild robot manipu- lation dataset,

Reference 40

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Observation 58cc99cf-5216-4b4e-9ed6-eb0907e52b96 · outbound

This paper cites Embodied hands: Modeling and capturing hands and bodies together,.

VLAff: Vision-Language-Affordance Model for Unified Actionable Affordances Embodied hands: Modeling and capturing hands and bodies together,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T18:01:17.484941Z

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Observation f73652f3-7549-4cf5-b5f7-eb0aab744698 · outbound

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

VLAff: Vision-Language-Affordance Model for Unified Actionable Affordances On the continuity of rotation representations in neural networks,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T18:01:17.477628Z

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Observation adc1acb2-2eac-4a54-aaae-e125d852bd85 · outbound

This paper cites Understanding human hands in contact at internet scale,.

VLAff: Vision-Language-Affordance Model for Unified Actionable Affordances Understanding human hands in contact at internet scale,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T18:01:17.469017Z

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Observation a13e96ae-e07b-4b61-b85f-7cf605259994 · outbound

This paper cites SAM 2: Segment Anything in Images and Videos.

VLAff: Vision-Language-Affordance Model for Unified Actionable Affordances SAM 2: Segment Anything in Images and Videos

Reference 44

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Observation 337de9e0-c609-4eae-a8f2-0ee84ca9dd0f · outbound

This paper cites Vitpose: Simple vision transformer baselines for human pose estimation,.

VLAff: Vision-Language-Affordance Model for Unified Actionable Affordances Vitpose: Simple vision transformer baselines for human pose estimation,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T18:01:17.460517Z

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Observation 2d4fac88-8ece-43a2-b2b5-32513591a9f8 · outbound

This paper cites CoTracker3: Simpler and Better Point Tracking by Pseudo-Labelling Real Videos.

VLAff: Vision-Language-Affordance Model for Unified Actionable Affordances CoTracker3: Simpler and Better Point Tracking by Pseudo-Labelling Real Videos

Reference 46

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Observation e644f7fe-012b-4b8d-838f-6ced7cd0d4b5 · outbound

This paper cites Partial Implementation of Max Flow and Min Cost Flow in Almost-Linear Time.

VLAff: Vision-Language-Affordance Model for Unified Actionable Affordances Partial Implementation of Max Flow and Min Cost Flow in Almost-Linear Time

Reference 47

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Observation ddd8f7f0-4eae-4885-b123-7ec83f4881a5 · outbound

This paper cites Egohos: Dataset and method for hand and object segmentation in egocentric videos,.

VLAff: Vision-Language-Affordance Model for Unified Actionable Affordances Egohos: Dataset and method for hand and object segmentation in egocentric videos,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T18:01:17.452196Z

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Observation cb7605e1-a217-4fb8-9ca8-f5a21f108487 · outbound

This paper cites ProPainter: Improving Propagation and Transformer for Video Inpainting.

VLAff: Vision-Language-Affordance Model for Unified Actionable Affordances ProPainter: Improving Propagation and Transformer for Video Inpainting

Reference 49

Resolution: verified exact; local_arxiv, observed 2026-08-08T18:01:16.971566Z

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Observation eecb27d4-4607-4903-aad2-264f6ab5d6c0 · outbound

This paper cites MoGe-2: Accurate Monocular Geometry with Metric Scale and Sharp Details.

VLAff: Vision-Language-Affordance Model for Unified Actionable Affordances MoGe-2: Accurate Monocular Geometry with Metric Scale and Sharp Details

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-08T18:01:16.698219Z

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This paper cites Structure-from-motion revisited,.

VLAff: Vision-Language-Affordance Model for Unified Actionable Affordances Structure-from-motion revisited,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T18:01:17.443547Z

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Observation b735d328-27e1-415b-974e-7dfae58a4a13 · outbound

This paper cites Droid-slam: Deep visual slam for monocular, stereo, and rgb-d cameras,.

VLAff: Vision-Language-Affordance Model for Unified Actionable Affordances Droid-slam: Deep visual slam for monocular, stereo, and rgb-d cameras,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T18:01:17.404213Z

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This paper cites Cosmological parameters estimated from velocity -- density comparisons: Calibrating 2M++.

VLAff: Vision-Language-Affordance Model for Unified Actionable Affordances Cosmological parameters estimated from velocity -- density comparisons: Calibrating 2M++

Reference 53

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This paper cites Lisa: Reasoning segmentation via large language model,.

VLAff: Vision-Language-Affordance Model for Unified Actionable Affordances Lisa: Reasoning segmentation via large language model,

Reference 54

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This paper cites DINOv2: Learning Robust Visual Features without Supervision.

VLAff: Vision-Language-Affordance Model for Unified Actionable Affordances DINOv2: Learning Robust Visual Features without Supervision

Reference 55

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This paper cites Qwen2.5-VL Technical Report.

VLAff: Vision-Language-Affordance Model for Unified Actionable Affordances Qwen2.5-VL Technical Report

Reference 56

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This paper cites Handal: A dataset of real-world manipulable object categories with pose annotations, affordances, and reconstructions,.

VLAff: Vision-Language-Affordance Model for Unified Actionable Affordances Handal: A dataset of real-world manipulable object categories with pose annotations, affordances, and reconstructions,

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T18:01:17.322968Z

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This paper cites Scenefun3d: Fine-grained functionality and affor- dance understanding in 3d scenes,.

VLAff: Vision-Language-Affordance Model for Unified Actionable Affordances Scenefun3d: Fine-grained functionality and affor- dance understanding in 3d scenes,

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T18:01:17.283009Z

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This paper cites Understanding 3d object interaction from a single image,.

VLAff: Vision-Language-Affordance Model for Unified Actionable Affordances Understanding 3d object interaction from a single image,

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T18:01:17.266440Z

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This paper cites RAM: Retrieval-Based Affordance Transfer for Generalizable Zero-Shot Robotic Manipulation.

VLAff: Vision-Language-Affordance Model for Unified Actionable Affordances RAM: Retrieval-Based Affordance Transfer for Generalizable Zero-Shot Robotic Manipulation

Reference 60

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Observation 10f87d64-38e4-41bd-83c1-e9b623a85cea · outbound

This paper cites Generalflow: Generalizable manipulation policy with flow matching,.

VLAff: Vision-Language-Affordance Model for Unified Actionable Affordances Generalflow: Generalizable manipulation policy with flow matching,

Reference 61

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This paper cites Graspnet-1billion: A large-scale benchmark for general object grasping,.

VLAff: Vision-Language-Affordance Model for Unified Actionable Affordances Graspnet-1billion: A large-scale benchmark for general object grasping,

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T18:01:17.257624Z

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This paper cites R+x: Retrieval and execution from everyday human videos,.

VLAff: Vision-Language-Affordance Model for Unified Actionable Affordances R+x: Retrieval and execution from everyday human videos,

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T18:01:17.249151Z

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This paper cites Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning.

VLAff: Vision-Language-Affordance Model for Unified Actionable Affordances Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning

Reference 64

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This paper cites Partmanip: Learning cross-category generalizable part manipulation policy from point cloud observations,.

VLAff: Vision-Language-Affordance Model for Unified Actionable Affordances Partmanip: Learning cross-category generalizable part manipulation policy from point cloud observations,

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T18:01:17.239675Z

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This paper cites Relay policy learning: Solving long-horizon tasks via imitation and reinforcement learning,.

VLAff: Vision-Language-Affordance Model for Unified Actionable Affordances Relay policy learning: Solving long-horizon tasks via imitation and reinforcement learning,

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T18:01:17.230123Z

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This paper cites Maniskill2: A unified benchmark for generalizable manipulation skills,.

VLAff: Vision-Language-Affordance Model for Unified Actionable Affordances Maniskill2: A unified benchmark for generalizable manipulation skills,

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T18:01:17.220850Z

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Observation c3de1a73-28dc-4f25-afe4-a991ea4a3205 · outbound

This paper cites Ag2manip: Learning novel manipulation skills with agent-agnostic visual and action representations,.

VLAff: Vision-Language-Affordance Model for Unified Actionable Affordances Ag2manip: Learning novel manipulation skills with agent-agnostic visual and action representations,

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T18:01:17.211220Z

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Observation 97febfe7-476a-474b-bd2e-eb2a8fc6ef54 · outbound

This paper cites Pointllm: Empowering large language models to understand point clouds,.

VLAff: Vision-Language-Affordance Model for Unified Actionable Affordances Pointllm: Empowering large language models to understand point clouds,

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T18:01:17.200369Z

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Observation a4e6b661-3308-4060-b8a1-0d8ecb8ecdc7 · outbound

This paper cites Generating 6dof object manipulation trajectories from action description in egocentric vision,.

VLAff: Vision-Language-Affordance Model for Unified Actionable Affordances Generating 6dof object manipulation trajectories from action description in egocentric vision,

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T18:01:17.190849Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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