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Paper Citation Record · LEDGER

Manipulate-Anything: Automating Real-World Robots using Vision-Language Models

As of 21 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 33 inbound Pith citation observations for arXiv:2406.18915.

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

pith.paper-citation-record.v1
2406.18915 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 33 of 33 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 33 of 33 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T21:24:01.038681Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-07-04T16:49:58.284739Z

Reference resolution

0 of 0 outbound references displayed

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External citation measurements

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Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 4e5014d3-1db7-4b63-bdeb-f3bb7e8d4116 · inbound

ReKep: Spatio-Temporal Reasoning of Relational Keypoint Constraints for Robotic Manipulation cites this paper.

ReKep: Spatio-Temporal Reasoning of Relational Keypoint Constraints for Robotic Manipulation Manipulate-Anything: Automating Real-World Robots using Vision-Language Models

Reference 113

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arxiv_id, observed 2026-05-16T08:25:17.997220Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-05-16T08:25:17.847571Z digest=sha256:8ba9516b5c793019b55e80b2722123184d4c92eeeed89b7a8282369f51f8c267

Observation 5b74727e-052e-4cf3-a15d-b63254ba76b3 · inbound

Semantic-Geometric-Physical-Driven Robot Manipulation Skill Transfer via Skill Library and Tactile Representation cites this paper.

Semantic-Geometric-Physical-Driven Robot Manipulation Skill Transfer via Skill Library and Tactile Representation Manipulate-Anything: Automating Real-World Robots using Vision-Language Models

Reference 7

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source=pdf_text observed=2026-08-12T18:17:08.518625Z digest=sha256:b6f023d2c1af8fc93bf152e317ccb43302f39efccd60a7aa8732067ee8cf287a

Observation 56a56a20-dae2-4dc6-b240-e5db4941b638 · inbound

MALMM: Multi-Agent Large Language Models for Zero-Shot Robotics Manipulation cites this paper.

MALMM: Multi-Agent Large Language Models for Zero-Shot Robotics Manipulation Manipulate-Anything: Automating Real-World Robots using Vision-Language Models

Reference 34

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source=pdf_text observed=2026-08-12T11:57:03.982774Z digest=sha256:c8b29c5d90202d400c1ffd35b753a67067a9fef21fd787664451047948329749

Observation ce7e5894-ac42-43bd-a96d-7e0cc1d9fdd2 · inbound

RoboMatrix: A Skill-centric Hierarchical Framework for Scalable Robot Task Planning and Execution in Open-World cites this paper.

RoboMatrix: A Skill-centric Hierarchical Framework for Scalable Robot Task Planning and Execution in Open-World Manipulate-Anything: Automating Real-World Robots using Vision-Language Models

Reference 32

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source=pdf_text observed=2026-08-12T05:47:13.737186Z digest=sha256:f079899026e19c6501933464047344ff75565a7c7ac97df4b636c67f800413da

Observation 984f5298-ad74-4221-a1b2-269510af16ab · inbound

The One RING: a Robotic Indoor Navigation Generalist cites this paper.

The One RING: a Robotic Indoor Navigation Generalist Manipulate-Anything: Automating Real-World Robots using Vision-Language Models

Reference 18

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source=pdf_text observed=2026-08-11T12:20:27.997034Z digest=sha256:29fe8e860993e77dc0f03b8aaadba28701decf4997bfbdc4510a71c72af8d6ae

Observation 8343e04a-d7ee-4859-ae16-2c531340e53c · inbound

CoA-VLA: Improving Vision-Language-Action Models via Visual-Textual Chain-of-Affordance cites this paper.

CoA-VLA: Improving Vision-Language-Action Models via Visual-Textual Chain-of-Affordance Manipulate-Anything: Automating Real-World Robots using Vision-Language Models

Reference 11

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source=pdf_text observed=2026-08-10T23:26:37.349740Z digest=sha256:57482bd45207c3ec16854ec844125ffa07abb3c7f42b66324bc4d9aa9bd2e557

Observation 2cdda455-de19-4850-b7b1-f509ebbd4f5d · inbound

OmniManip: Towards General Robotic Manipulation via Object-Centric Interaction Primitives as Spatial Constraints cites this paper.

OmniManip: Towards General Robotic Manipulation via Object-Centric Interaction Primitives as Spatial Constraints Manipulate-Anything: Automating Real-World Robots using Vision-Language Models

Reference 9

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source=pdf_text observed=2026-08-10T21:50:41.063835Z digest=sha256:7e7b041b14ead2a235281f1e1076f307231f7cd11c2da47b4cf8e8c517e1aecb

Observation 048138fa-6501-48b8-8cf9-c2214ebe94de · inbound

GeoManip: Geometric Constraints as General Interfaces for Robot Manipulation cites this paper.

GeoManip: Geometric Constraints as General Interfaces for Robot Manipulation Manipulate-Anything: Automating Real-World Robots using Vision-Language Models

Reference 17

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source=arxiv_source observed=2026-08-10T19:47:36.443119Z digest=sha256:51cf4ba1b45378d9d20ac5176921ea7cce3260fae880986710cb453198ed0723

Observation 4a0c8040-e777-4704-85ac-125c5d9efdd4 · inbound

SAM2Act: Integrating Visual Foundation Model with A Memory Architecture for Robotic Manipulation cites this paper.

SAM2Act: Integrating Visual Foundation Model with A Memory Architecture for Robotic Manipulation Manipulate-Anything: Automating Real-World Robots using Vision-Language Models

Reference 12

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no resolver link, observed 2026-08-09T23:04:14.511420Z

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source=pdf_text observed=2026-08-09T23:04:14.511420Z digest=sha256:7566ab070f9c6303964c585f7048b2e8bb9cf26321b8e00739686fadf30008d9

Observation 44dc6a78-0b65-48c8-bf06-bc23b98a7823 · inbound

A Real-to-Sim-to-Real Approach to Robotic Manipulation with VLM-Generated Iterative Keypoint Rewards cites this paper.

A Real-to-Sim-to-Real Approach to Robotic Manipulation with VLM-Generated Iterative Keypoint Rewards Manipulate-Anything: Automating Real-World Robots using Vision-Language Models

Reference 33

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source=pdf_text observed=2026-08-08T00:03:16.439194Z digest=sha256:03bc690e2e36e5a88f59b668a2a6e93ea6833e417cbecb0fea5b116ca6899869

Observation 8086946b-1f71-413a-8ee3-2c933173383d · inbound

Fine-Tuning Vision-Language-Action Models: Optimizing Speed and Success cites this paper.

Fine-Tuning Vision-Language-Action Models: Optimizing Speed and Success Manipulate-Anything: Automating Real-World Robots using Vision-Language Models

Reference 6

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arxiv_id, observed 2026-05-11T04:35:32.300990Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-05-11T04:35:31.914360Z digest=sha256:319811c3100c1951e3783ddb523ef155096a28f47a59674de006f6d9113e6985

Observation cb2b7158-1d5a-45ee-b1c1-b61fcee8e625 · inbound

$\pi_{0.5}$: a Vision-Language-Action Model with Open-World Generalization cites this paper.

$\pi_{0.5}$: a Vision-Language-Action Model with Open-World Generalization Manipulate-Anything: Automating Real-World Robots using Vision-Language Models

Reference 24

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arxiv_id, observed 2026-05-22T18:05:00.865624Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-05-22T18:02:23.305313Z digest=sha256:b8a9bed34839fbec84d62ac0c01ac5be7534c4ef7878b7948689b6b7c0349d9b

Observation 63440c4a-c670-41f1-904c-b6789f1fb571 · inbound

PointArena: Probing Multimodal Grounding Through Language-Guided Pointing cites this paper.

PointArena: Probing Multimodal Grounding Through Language-Guided Pointing Manipulate-Anything: Automating Real-World Robots using Vision-Language Models

Reference 13

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source=arxiv_source observed=2026-08-15T21:24:01.038681Z digest=sha256:32af99c17ad6def44856272b68aafc5f2b5762387c5597a89eee6321c5d8dfae

Observation 2b06627f-d851-43ab-b2ae-98ebc4d78c9c · inbound

GraspMolmo: Generalizable Task-Oriented Grasping via Large-Scale Synthetic Data Generation cites this paper.

GraspMolmo: Generalizable Task-Oriented Grasping via Large-Scale Synthetic Data Generation Manipulate-Anything: Automating Real-World Robots using Vision-Language Models

Reference 24

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source=pdf_text observed=2026-08-15T20:18:08.138954Z digest=sha256:10e3096b9aa2e7efe89ccae99f6230fb3243c6acad2eb7462cf4c2ff01193bc8

Observation 8df6e184-76dd-4822-a79d-9717c2273671 · inbound

Think Twice, Act Once: Token-Aware Compression and Action Reuse for Efficient Inference in Vision-Language-Action Models cites this paper.

Think Twice, Act Once: Token-Aware Compression and Action Reuse for Efficient Inference in Vision-Language-Action Models Manipulate-Anything: Automating Real-World Robots using Vision-Language Models

Reference 13

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source=pdf_text observed=2026-08-07T13:43:00.230985Z digest=sha256:6db4b308fd6b00ae19d7d5cf9e9817ebd14b8d6a5c517e063a39211acf02ccca

Observation 27568cc8-580a-4f94-825d-425b2dfbe13e · inbound

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

UAD: Unsupervised Affordance Distillation for Generalization in Robotic Manipulation Manipulate-Anything: Automating Real-World Robots using Vision-Language Models

Reference 105

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source=pdf_text observed=2026-08-07T04:58:54.815582Z digest=sha256:619e098c7802cfa8867a46d956af0b5515ddc3a79dd61fdb6f0279b781bb27db

Observation cccfe068-1b98-40b2-8c65-3d6f8c0ad2a7 · inbound

GENMANIP: LLM-driven Simulation for Generalizable Instruction-Following Manipulation cites this paper.

GENMANIP: LLM-driven Simulation for Generalizable Instruction-Following Manipulation Manipulate-Anything: Automating Real-World Robots using Vision-Language Models

Reference 12

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source=pdf_text observed=2026-08-07T04:17:07.939920Z digest=sha256:c1fbf3c2820eb32f6335344b6e0ae485cf5a465a6c2eeb3bbb75ba973c37a948

Observation 44cfb571-0f28-4941-8826-e53bf5b252d0 · inbound

AntiGrounding: Lifting Robotic Actions into VLM Representation Space for Decision Making cites this paper.

AntiGrounding: Lifting Robotic Actions into VLM Representation Space for Decision Making Manipulate-Anything: Automating Real-World Robots using Vision-Language Models

Reference 53

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source=pdf_text observed=2026-08-07T00:57:29.714057Z digest=sha256:f20563733138504553e56629c9d6bf4a299a0e5dfd639745befe30fb6ef27fa7

Observation 918dbb7c-9893-460a-93fa-65838e0ee425 · inbound

Prompting with the Future: Open-World Model Predictive Control with Interactive Digital Twins cites this paper.

Prompting with the Future: Open-World Model Predictive Control with Interactive Digital Twins Manipulate-Anything: Automating Real-World Robots using Vision-Language Models

Reference 12

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source=pdf_text observed=2026-08-07T00:31:00.333680Z digest=sha256:3e2185ccc7d2bdd4f6a471d81216857fa07aeec18630ca1e32ba23d8ca7678a6

Observation 740a81f1-f0aa-4e87-94c5-4884104d18fe · inbound

CodeDiffuser: Attention-Enhanced Diffusion Policy via VLM-Generated Code for Instruction Ambiguity cites this paper.

CodeDiffuser: Attention-Enhanced Diffusion Policy via VLM-Generated Code for Instruction Ambiguity Manipulate-Anything: Automating Real-World Robots using Vision-Language Models

Reference 74

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no resolver link, observed 2026-08-15T19:31:53.488191Z

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source=arxiv_source observed=2026-08-15T19:31:53.488191Z digest=sha256:646b9c1f18f484cb12d59ab9f1b41f04497c01d8ba9116eb07b55b924366cf27

Observation 3979c744-f296-4f92-9d02-18859cb8e143 · inbound

LMPVC and Policy Bank: Adaptive voice control for industrial robots with code generating LLMs and reusable Pythonic policies cites this paper.

LMPVC and Policy Bank: Adaptive voice control for industrial robots with code generating LLMs and reusable Pythonic policies Manipulate-Anything: Automating Real-World Robots using Vision-Language Models

Reference 15

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source=pdf_text observed=2026-08-06T22:16:28.523439Z digest=sha256:6714beec8553d06ce061da8d1074147125f8247d306a79a330752a73a8d4222d

Observation 128838ac-81bb-4e6d-a411-457e345dfcaa · inbound

SimLauncher: Launching Sample-Efficient Real-world Robotic Reinforcement Learning via Simulation Pre-training cites this paper.

SimLauncher: Launching Sample-Efficient Real-world Robotic Reinforcement Learning via Simulation Pre-training Manipulate-Anything: Automating Real-World Robots using Vision-Language Models

Reference 11

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source=pdf_text observed=2026-08-06T19:53:06.167701Z digest=sha256:4a53d83d742809b3ab508098d20c93f12d1ca78e0329bb6ac65b24f5460c4f58

Observation 41855b5a-9300-4fcf-be6d-bc55d47ec878 · inbound

Improving Generalization of Language-Conditioned Robot Manipulation cites this paper.

Improving Generalization of Language-Conditioned Robot Manipulation Manipulate-Anything: Automating Real-World Robots using Vision-Language Models

Reference 6

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source=arxiv_source observed=2026-08-06T04:59:59.476521Z digest=sha256:30f5bc2871e9c2cbdfb5e7df6868c9f409ea2bd6388ed97276b745b9994d17d2

Observation fec17863-6692-4e7c-ac09-bfc918ad9ed1 · inbound

Robix: A Unified Model for Robot Interaction, Reasoning and Planning cites this paper.

Robix: A Unified Model for Robot Interaction, Reasoning and Planning Manipulate-Anything: Automating Real-World Robots using Vision-Language Models

Reference 17

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source=pdf_text observed=2026-08-05T12:59:03.378073Z digest=sha256:887ab9dd7cc29dbc76d170e65f05407177ee29e12bfcb59e5b1d2f099e57be06

Observation aef4fe6e-e708-439a-9981-e30320804c57 · inbound

PLanAR: Planning-Language-Grounded Agentic Reasoning for Robot Manipulation cites this paper.

PLanAR: Planning-Language-Grounded Agentic Reasoning for Robot Manipulation Manipulate-Anything: Automating Real-World Robots using Vision-Language Models

Reference 6

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no resolver link, observed 2026-08-03T05:38:48.119729Z

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source=pdf_text observed=2026-08-03T05:38:48.119729Z digest=sha256:f163ee33942cde72656ebd83341df030e4c3e2ef582575ab7616c853015b9608

Observation 258eda24-71e1-4db4-b709-266ba4c08276 · inbound

From Reaction to Anticipation: Proactive Failure Recovery through Agentic Task Graph for Robotic Manipulation cites this paper.

From Reaction to Anticipation: Proactive Failure Recovery through Agentic Task Graph for Robotic Manipulation Manipulate-Anything: Automating Real-World Robots using Vision-Language Models

Reference 14

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arxiv_id, observed 2026-05-13T05:17:18.319566Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-05-13T05:14:45.978785Z digest=sha256:591b7fc5937f365eec6959309e7eb82e115346b621b3f74165c3dcbed545aa64

Observation be071f9e-acf9-47a3-a89e-85e578f014c3 · inbound

DyGRO-VLA: Cross-Task Scaling of Vision-Language-Action Models via Dynamic Grouped Residual Optimization cites this paper.

DyGRO-VLA: Cross-Task Scaling of Vision-Language-Action Models via Dynamic Grouped Residual Optimization Manipulate-Anything: Automating Real-World Robots using Vision-Language Models

Reference 51

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arxiv_id, observed 2026-05-20T12:43:17.134957Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-05-20T12:39:50.004269Z digest=sha256:41e7b9e0517f501da516846512f4f0f17771bf011bd6912a1156c6f7b0af38c6

Observation b05f63cf-9343-40ec-b9e6-1599bb5b04db · inbound

InSight: Self-Guided Skill Acquisition via Steerable VLAs cites this paper.

InSight: Self-Guided Skill Acquisition via Steerable VLAs Manipulate-Anything: Automating Real-World Robots using Vision-Language Models

Reference 33

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arxiv_id, observed 2026-07-04T16:49:58.286500Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-06-26T00:10:51.721485Z digest=sha256:5cf884c59e26189ceb99c8b7a480a6583f57cdee604e7bbae9b70905dba44219

Observation 2f3cee57-a459-4151-8fad-2bff9ce64411 · inbound

CoStream: Composing Simple Behaviors for Generalizable Complex Manipulation cites this paper.

CoStream: Composing Simple Behaviors for Generalizable Complex Manipulation Manipulate-Anything: Automating Real-World Robots using Vision-Language Models

Reference 22

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arxiv_id, observed 2026-07-04T15:49:56.950990Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-06-26T01:25:21.796778Z digest=sha256:cc3321ee9829a017a2752a2db7f0ba32ef0f38ee7b671ef8d93904a5174279ea

Observation a3817e86-f094-4f83-9a95-38059f124cf4 · inbound

CoStream: Composing Simple Behaviors for Generalizable Complex Manipulation cites this paper.

CoStream: Composing Simple Behaviors for Generalizable Complex Manipulation Manipulate-Anything: Automating Real-World Robots using Vision-Language Models

Reference 22

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arxiv_id, observed 2026-07-01T16:05:49.685941Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-06-30T00:54:50.393828Z digest=sha256:f18b7e1155dc17dd3fb9ea279bb5d677d2d28bdba4dfa279021fe6d7d0c594a0

Observation b7072e8d-5f72-49ab-b1d6-503fde7abe84 · inbound

RT-SHCUA: Real-Time Self-Hosted Computer-Use Agent for UAV Control cites this paper.

RT-SHCUA: Real-Time Self-Hosted Computer-Use Agent for UAV Control Manipulate-Anything: Automating Real-World Robots using Vision-Language Models

Reference 21

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source=pdf_text observed=2026-08-01T16:35:14.441630Z digest=sha256:1e4d0660f98039c26bcf985f86e6878031c82fe0918dbb5821b10234ddaa4752

Observation 393955f4-c1c6-4716-a25e-db95a001a529 · inbound

RoboInter1.5: A Holistic Intermediate Representation Suite for Embodied World Modeling and Robotic Manipulation cites this paper.

RoboInter1.5: A Holistic Intermediate Representation Suite for Embodied World Modeling and Robotic Manipulation Manipulate-Anything: Automating Real-World Robots using Vision-Language Models

Reference 122

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no resolver link, observed 2026-08-01T14:39:45.808344Z

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source=arxiv_source observed=2026-08-01T14:39:45.808344Z digest=sha256:2a7dc47e0e6a0b2c9302d2ba499ee0710a2f6316815cac6eeb3bc93af812b061

Observation 13fd7571-e194-42a0-b5bf-96ad2e29f2de · inbound

World Action Planner: Generalizable Decision-Making with Action-Conditioned World Models cites this paper.

World Action Planner: Generalizable Decision-Making with Action-Conditioned World Models Manipulate-Anything: Automating Real-World Robots using Vision-Language Models

Reference 12

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source=pdf_text observed=2026-08-01T04:59:30.918558Z digest=sha256:3e250916b4db7244c6374a30e99e4bf1dbfccf5239d88fb17dcf970b46d2454a