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Paper Citation Record · LEDGER

Hy-Embodied-0.5-VLA: From Vision-Language-Action Models to a Real-World Robot Learning Stack

As of 18 August 2026, this Paper Citation Record lists 59 of 59 outbound references and 7 inbound Pith citation observations for arXiv:2606.14409.

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

pith.paper-citation-record.v1
2606.14409 v2

Coverage vector

measured 59 of 59 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-02T11:31:38.052183Z

measured 66 of 66 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 7 of 7 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T14:52:22.168086Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T17:58:47.581063Z

Reference resolution

59 of 59 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved59
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 94afc17a-f003-4b04-a8ee-fb99ac0741ac · outbound

This paper cites $\pi_0$: A Vision-Language-Action Flow Model for General Robot Control.

Hy-Embodied-0.5-VLA: From Vision-Language-Action Models to a Real-World Robot Learning Stack $\pi_0$: A Vision-Language-Action Flow Model for General Robot Control

Reference 1

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source=pdf_text observed=2026-08-02T11:31:33.184029Z digest=sha256:218c9922ea6aa63d8f3c729de609bee59255b210f5e34b6f2367f6b005903eca

Observation 45c6e1ed-1768-497c-911e-43286859d14b · outbound

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

Hy-Embodied-0.5-VLA: From Vision-Language-Action Models to a Real-World Robot Learning Stack $\pi_{0.5}$: a Vision-Language-Action Model with Open-World Generalization

Reference 2

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source=pdf_text observed=2026-08-02T11:31:33.267653Z digest=sha256:57d3f95b742d1b92abd5cc4ecdb207d635c77b383b96eb6e3a61a7553a457cea

Observation 7874eeb9-fb50-463a-ab99-829a8de419db · outbound

This paper cites Gemini Robotics: Bringing AI into the Physical World.

Hy-Embodied-0.5-VLA: From Vision-Language-Action Models to a Real-World Robot Learning Stack Gemini Robotics: Bringing AI into the Physical World

Reference 3

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source=pdf_text observed=2026-08-02T11:31:33.314639Z digest=sha256:24acb5c5a8cbcbd50875fa3bc3e38e4688e48321316c95cb5af8616bca3dd895

Observation 0524bf3f-8e8e-4d1d-b2d8-e09259cf5ef4 · outbound

This paper cites GR00T N1: An Open Foundation Model for Generalist Humanoid Robots.

Hy-Embodied-0.5-VLA: From Vision-Language-Action Models to a Real-World Robot Learning Stack GR00T N1: An Open Foundation Model for Generalist Humanoid Robots

Reference 4

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source=pdf_text observed=2026-08-02T11:31:33.386529Z digest=sha256:36a6e0b9ef0075744235a8ae8297b7d90a48ff5fb84f26d96c69ba97ffdb4ff9

Observation f46377fb-4004-4e91-9070-1c3c90f562f2 · outbound

This paper cites Universal pose pretraining for generalizable vision-language-action policies.

Hy-Embodied-0.5-VLA: From Vision-Language-Action Models to a Real-World Robot Learning Stack Universal pose pretraining for generalizable vision-language-action policies

Reference 5

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source=pdf_text observed=2026-08-02T11:31:33.483759Z digest=sha256:95236773633c29c5470fe29ca28488cf7e8ea0e14bfe1132e6fcbe53f804fd2d

Observation fc79ab49-c0f3-432e-84db-1be1c76d50e6 · outbound

This paper cites Rdt-1b: a diffusion foundation model for bimanual manipulation.

Hy-Embodied-0.5-VLA: From Vision-Language-Action Models to a Real-World Robot Learning Stack Rdt-1b: a diffusion foundation model for bimanual manipulation

Reference 6

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source=pdf_text observed=2026-08-02T11:31:33.587583Z digest=sha256:d2d5bea658b5bb5a421e8271ae6ec7ec022cdc0ad2b89ea46cc919b49b28bd01

Observation 6361d3e5-9029-47ca-83ae-b6fa77a0dee4 · outbound

This paper cites Learningfine-grainedbimanual manipulation with low-cost hardware.

Hy-Embodied-0.5-VLA: From Vision-Language-Action Models to a Real-World Robot Learning Stack Learningfine-grainedbimanual manipulation with low-cost hardware

Reference 7

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source=pdf_text observed=2026-08-02T11:31:33.633171Z digest=sha256:00ff905c51580c8d468b38c8b8aecc00ec83955c1c64f8200f746a8593dd7a33

Observation eedd00fd-10fd-4e3c-9bd6-3361a0385231 · outbound

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

Hy-Embodied-0.5-VLA: From Vision-Language-Action Models to a Real-World Robot Learning Stack Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware

Reference 8

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source=pdf_text observed=2026-08-02T11:31:33.729851Z digest=sha256:7e41b040009f87d6c712c64f6d5d21846d9e6b482aebc82d39e7a7ff878ad72c

Observation b0a28147-1a24-4c47-aa17-1de436a5d9a1 · outbound

This paper cites EgoVLA: Learning Vision-Language-Action Models from Egocentric Human Videos.

Hy-Embodied-0.5-VLA: From Vision-Language-Action Models to a Real-World Robot Learning Stack EgoVLA: Learning Vision-Language-Action Models from Egocentric Human Videos

Reference 9

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source=pdf_text observed=2026-08-02T11:31:33.814742Z digest=sha256:a6497d2695799871073289599dc991464cc13d0a7f105c3a5951186831de8a0a

Observation dfd02d4c-9903-4d68-b27d-b050b2b5d047 · outbound

This paper cites Scalable vision-language-action model pretraining for robotic manipulation with real-life human activity videos.arXiv preprint arXiv:2510.21571, 2025.

Hy-Embodied-0.5-VLA: From Vision-Language-Action Models to a Real-World Robot Learning Stack Scalable vision-language-action model pretraining for robotic manipulation with real-life human activity videos.arXiv preprint arXiv:2510.21571, 2025

Reference 10

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source=pdf_text observed=2026-08-02T11:31:33.910138Z digest=sha256:1a8dd30224b507acc047a07ba196d138d3a9656519c625bdbc0e5af4f78ee7b7

Observation 7f4a8db4-ee1c-494d-bcfb-9939e13abb43 · outbound

This paper cites Universalmanipulationinterface:In-the-wildrobot teachingwithout in-the-wild robots.

Hy-Embodied-0.5-VLA: From Vision-Language-Action Models to a Real-World Robot Learning Stack Universalmanipulationinterface:In-the-wildrobot teachingwithout in-the-wild robots

Reference 11

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source=pdf_text observed=2026-08-02T11:31:33.947800Z digest=sha256:731cb0b6c72a3b78e099815a65b4d93872fe71addfee8404c6eaf70861de601f

Observation 95fc349b-3599-4e89-8c9c-958dede8c1f9 · outbound

This paper cites RT-2:Vision-language-action models transfer web knowledge to robotic control.

Hy-Embodied-0.5-VLA: From Vision-Language-Action Models to a Real-World Robot Learning Stack RT-2:Vision-language-action models transfer web knowledge to robotic control

Reference 12

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source=pdf_text observed=2026-08-02T11:31:34.025387Z digest=sha256:a65e60895192894b7a0b482dc3e369746f1718e012f3cfcacca68319745a2d39

Observation 50ebb30d-8d3d-4280-918c-7e8035ed9403 · outbound

This paper cites OpenVLA: An open-source vision-language-action model.

Hy-Embodied-0.5-VLA: From Vision-Language-Action Models to a Real-World Robot Learning Stack OpenVLA: An open-source vision-language-action model

Reference 13

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source=pdf_text observed=2026-08-02T11:31:34.126757Z digest=sha256:23fb7a5a51c4b32f4612d7e2b8fb2578dc5d62843800f4b7428bf56e2e9f1d63

Observation 9e0cae91-1e55-4460-a8ef-0c6865aff714 · outbound

This paper cites $\pi^{*}_{0.6}$: a VLA That Learns From Experience.

Hy-Embodied-0.5-VLA: From Vision-Language-Action Models to a Real-World Robot Learning Stack $\pi^{*}_{0.6}$: a VLA That Learns From Experience

Reference 14

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source=pdf_text observed=2026-08-02T11:31:34.198419Z digest=sha256:8d1e62364f0ca27b27c32135246bc36339cd310cd0f9750756401090f859399c

Observation 30bada3c-3e97-493e-9f91-a54c9998d64a · outbound

This paper cites Hy-Embodied-0.5: Embodied foundation models for real-world agents.

Hy-Embodied-0.5-VLA: From Vision-Language-Action Models to a Real-World Robot Learning Stack Hy-Embodied-0.5: Embodied foundation models for real-world agents

Reference 15

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source=pdf_text observed=2026-08-02T11:31:34.313415Z digest=sha256:8e5c9ba150832c4fbf0270bd62311cec388e5599d3e7b2521ce0fdc747c1d61a

Observation 7b4ab863-bfe2-4ead-8a05-6b3f17ea3036 · outbound

This paper cites PaliGemma: A versatile 3B VLM for transfer.

Hy-Embodied-0.5-VLA: From Vision-Language-Action Models to a Real-World Robot Learning Stack PaliGemma: A versatile 3B VLM for transfer

Reference 16

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source=pdf_text observed=2026-08-02T11:31:34.367603Z digest=sha256:6e70277e7867b825231d6f0e313db96f1608b457fd99a89e6a6b391270a48aa9

Observation 870da128-f1a9-422e-8b50-cac0d040177f · outbound

This paper cites Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution.

Hy-Embodied-0.5-VLA: From Vision-Language-Action Models to a Real-World Robot Learning Stack Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution

Reference 17

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source=pdf_text observed=2026-08-02T11:31:34.435753Z digest=sha256:53db821c2f7d182476b77f1395a6e6bff31f1bb62bd5a6c651015b80c43ba94b

Observation 171f1d79-6b59-477e-a1a0-3e6a776128c1 · outbound

This paper cites Eagle 2: Building Post-Training Data Strategies from Scratch for Frontier Vision-Language Models.

Hy-Embodied-0.5-VLA: From Vision-Language-Action Models to a Real-World Robot Learning Stack Eagle 2: Building Post-Training Data Strategies from Scratch for Frontier Vision-Language Models

Reference 18

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source=pdf_text observed=2026-08-02T11:31:34.532750Z digest=sha256:9f78bf2c9b670676d91f231321c005a13d2ff6ffbae9b7cb1f57f87a9ad0efae

Observation 3329dd95-3ae2-4c66-811f-3187e50d2f02 · outbound

This paper cites FlowPRO: Reward-Free Reinforced Fine-Tuning of Flow-Matching VLAs via Proximalized Preference Optimization.

Hy-Embodied-0.5-VLA: From Vision-Language-Action Models to a Real-World Robot Learning Stack FlowPRO: Reward-Free Reinforced Fine-Tuning of Flow-Matching VLAs via Proximalized Preference Optimization

Reference 19

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source=pdf_text observed=2026-08-02T11:31:34.620402Z digest=sha256:82148bb21388a0c677bb232a77e76106a257cb83c07dbbd25efe85b62ed01f24

Observation 7369cda2-6989-4374-a897-c5b7be7fd152 · outbound

This paper cites Mixture-of-Transformers: A Sparse and Scalable Architecture for Multi-Modal Foundation Models.

Hy-Embodied-0.5-VLA: From Vision-Language-Action Models to a Real-World Robot Learning Stack Mixture-of-Transformers: A Sparse and Scalable Architecture for Multi-Modal Foundation Models

Reference 20

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source=pdf_text observed=2026-08-02T11:31:34.781921Z digest=sha256:da670e36b06039a88e469a84bd882d858456591a9dd569123e8b64726499d78e

Observation 24cd364b-b6ec-45c8-bb8c-902a7b967cc2 · outbound

This paper cites an unresolved cited work.

Hy-Embodied-0.5-VLA: From Vision-Language-Action Models to a Real-World Robot Learning Stack Unresolved cited work

Reference 21

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source=pdf_text observed=2026-08-02T11:31:34.854747Z digest=sha256:c36da94a1aaf410fa6f28c4e50a0448ae93f46e96db74f92c3da44b30327c05d

Observation 2e642bcc-965d-4763-ab88-c6f59406c859 · outbound

This paper cites Multi-scale embodied memory for vision-language-action models.arXiv preprint arXiv:2603.03596, 2026.

Hy-Embodied-0.5-VLA: From Vision-Language-Action Models to a Real-World Robot Learning Stack Multi-scale embodied memory for vision-language-action models.arXiv preprint arXiv:2603.03596, 2026

Reference 22

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source=pdf_text observed=2026-08-02T11:31:34.959150Z digest=sha256:3495ce64a6997389ee06bc4c8c8d2e0223348d8567bf876be68ba1c16894a079

Observation 97c95388-0135-4ffe-9298-0db600ef27b8 · outbound

This paper cites Mode-adaptive neural networks for quadruped motion control.ACM Transactions on Graphics (ToG), 37(4):1–11, 2018.

Hy-Embodied-0.5-VLA: From Vision-Language-Action Models to a Real-World Robot Learning Stack Mode-adaptive neural networks for quadruped motion control.ACM Transactions on Graphics (ToG), 37(4):1–11, 2018

Reference 23

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source=pdf_text observed=2026-08-02T11:31:35.096860Z digest=sha256:c681f8f7ee15c1187f6d9492643793a15958edf9697d6bfba1c32fd85691f56c

Observation eec9ecd5-c9cb-47d0-b41a-393c33bba930 · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

Hy-Embodied-0.5-VLA: From Vision-Language-Action Models to a Real-World Robot Learning Stack An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 24

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source=pdf_text observed=2026-08-02T11:31:35.216880Z digest=sha256:b1b85e745a89f98b984714b471f6ae937cfb08f26ebdfee2b6902da10a97cca9

Observation 1e1020fe-9899-4b63-bcf8-dfbf07649fcc · outbound

This paper cites Patchn’pack:Navit,avisiontransformerforanyaspectratioandresolution.AdvancesinNeural Information Processing Systems, 36:2252–2274, 2023.

Hy-Embodied-0.5-VLA: From Vision-Language-Action Models to a Real-World Robot Learning Stack Patchn’pack:Navit,avisiontransformerforanyaspectratioandresolution.AdvancesinNeural Information Processing Systems, 36:2252–2274, 2023

Reference 25

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source=pdf_text observed=2026-08-02T11:31:35.284298Z digest=sha256:61f3f625cc24a0c7fbe5a725840be79e9c1c0b67a0a5b5ab7360636c9a076e23

Observation 1b4834d9-e0e3-48b2-a26b-8ece65518e48 · outbound

This paper cites Decoupled Weight Decay Regularization.

Hy-Embodied-0.5-VLA: From Vision-Language-Action Models to a Real-World Robot Learning Stack Decoupled Weight Decay Regularization

Reference 26

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source=pdf_text observed=2026-08-02T11:31:35.333827Z digest=sha256:8c5992125750455e40c92196fb21089a7d0d22ee54a528ebb3d30f0f1d20e417

Observation 1e28eb41-657e-4f9d-8e96-2ad7cfefdada · outbound

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

Hy-Embodied-0.5-VLA: From Vision-Language-Action Models to a Real-World Robot Learning Stack RoboTwin 2.0: A Scalable Data Generator and Benchmark with Strong Domain Randomization for Robust Bimanual Robotic Manipulation

Reference 27

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source=pdf_text observed=2026-08-02T11:31:35.396263Z digest=sha256:11a08495ada6434f78eec92987b9b06d8179b8e2b42630a34ead779526d919f2

Observation a8382ff1-fa55-4bda-82f6-bdb7c80b0594 · outbound

This paper cites Flow straight and fast: Learning to generate and transfer data with rectified flow.International Conference on Learning Representations (ICLR), 2023.

Hy-Embodied-0.5-VLA: From Vision-Language-Action Models to a Real-World Robot Learning Stack Flow straight and fast: Learning to generate and transfer data with rectified flow.International Conference on Learning Representations (ICLR), 2023

Reference 28

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source=pdf_text observed=2026-08-02T11:31:35.509690Z digest=sha256:1d191658528356e105f0ec42419b0ead695ea49c64e4530e5f4988460a68ef55

Observation e8cf599b-b197-4736-824e-a9ece5b996b8 · outbound

This paper cites Improving Video Generation with Human Feedback.

Hy-Embodied-0.5-VLA: From Vision-Language-Action Models to a Real-World Robot Learning Stack Improving Video Generation with Human Feedback

Reference 29

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source=pdf_text observed=2026-08-02T11:31:35.570110Z digest=sha256:1d57cdc0756f3e775a838761264ebc6317c717bb5e651e6bb0bbc098c76e3c94

Observation 5c1af7cf-3870-4a6b-8459-7321172c371c · outbound

This paper cites Proximalized preference optimization for diverse feedback types: A decomposed perspective on DPO.

Hy-Embodied-0.5-VLA: From Vision-Language-Action Models to a Real-World Robot Learning Stack Proximalized preference optimization for diverse feedback types: A decomposed perspective on DPO

Reference 30

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source=pdf_text observed=2026-08-02T11:31:35.630346Z digest=sha256:3d32976cfbefcaeedcb257311bca7c5cd07086b85607b484fa18e06294b54ce2

Observation e541f357-c7fc-435d-9000-74da34a65c78 · outbound

This paper cites ABot-M0: VLA Foundation Model for Robotic Manipulation with Action Manifold Learning.

Hy-Embodied-0.5-VLA: From Vision-Language-Action Models to a Real-World Robot Learning Stack ABot-M0: VLA Foundation Model for Robotic Manipulation with Action Manifold Learning

Reference 31

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source=pdf_text observed=2026-08-02T11:31:35.686670Z digest=sha256:18f7211c622ff11f92bf139f393cecb6b5ddb754535b0c902bdf5a732947610d

Observation 575ca4fb-98f2-4422-aa37-07b761179b1d · outbound

This paper cites Qwen-VLA: Unifying Vision-Language-Action Modeling across Tasks, Environments, and Robot Embodiments.

Hy-Embodied-0.5-VLA: From Vision-Language-Action Models to a Real-World Robot Learning Stack Qwen-VLA: Unifying Vision-Language-Action Modeling across Tasks, Environments, and Robot Embodiments

Reference 32

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source=pdf_text observed=2026-08-02T11:31:35.765477Z digest=sha256:caf0a88e9da16374c642cb27fd8444b3f268289df1ee7690b91875d9a321567e

Observation 9f7fdae9-34af-4925-a43f-829cc07a36eb · outbound

This paper cites A Pragmatic VLA Foundation Model.

Hy-Embodied-0.5-VLA: From Vision-Language-Action Models to a Real-World Robot Learning Stack A Pragmatic VLA Foundation Model

Reference 33

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source=pdf_text observed=2026-08-02T11:31:35.827769Z digest=sha256:12067707949f4ed060db46d4ef24aab8e2ddb9b599b60cb9ac2f23420b6ad9a1

Observation 251a2b84-2d2a-4e4b-80ab-a02fccad2719 · outbound

This paper cites StarVLA: A Lego-like Codebase for Vision-Language-Action Model Developing.

Hy-Embodied-0.5-VLA: From Vision-Language-Action Models to a Real-World Robot Learning Stack StarVLA: A Lego-like Codebase for Vision-Language-Action Model Developing

Reference 34

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source=pdf_text observed=2026-08-02T11:31:35.911047Z digest=sha256:0f9c35a56ce30122d9906fc681845c18dd91a9e330549eab7e4ee5365462a803

Observation bc205fed-e7eb-4f04-a25d-67c79a1e68a7 · outbound

This paper cites Motus: A Unified Latent Action World Model.

Hy-Embodied-0.5-VLA: From Vision-Language-Action Models to a Real-World Robot Learning Stack Motus: A Unified Latent Action World Model

Reference 35

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source=pdf_text observed=2026-08-02T11:31:35.975095Z digest=sha256:11f17050bff4b28f51dbcd468408197c7571900434d2cfde8c42166dad15ac27

Observation 4751e36a-1746-448c-be14-77ab8b48efab · outbound

This paper cites JoyAI-RA 0.1: A Foundation Model for Robotic Autonomy.

Hy-Embodied-0.5-VLA: From Vision-Language-Action Models to a Real-World Robot Learning Stack JoyAI-RA 0.1: A Foundation Model for Robotic Autonomy

Reference 36

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source=pdf_text observed=2026-08-02T11:31:36.042774Z digest=sha256:b1afa8feab27a444e92846390f957c553a3f5b43669e78477987155b5a9ab771

Observation 675391f3-76ab-418c-a189-2137423f2084 · outbound

This paper cites an unresolved cited work.

Hy-Embodied-0.5-VLA: From Vision-Language-Action Models to a Real-World Robot Learning Stack Unresolved cited work

Reference 37

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source=pdf_text observed=2026-08-02T11:31:36.095780Z digest=sha256:fcdf01c248157f1acc10c864f8ad1b0b08554fa7491bb913cf76a3eece4b6230

Observation da60f461-7009-4121-a428-50317619ac58 · outbound

This paper cites Gordon, and J.

Hy-Embodied-0.5-VLA: From Vision-Language-Action Models to a Real-World Robot Learning Stack Gordon, and J

Reference 38

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source=pdf_text observed=2026-08-02T11:31:36.176565Z digest=sha256:4dbecad1f9f56067c88579d825a234daf7098c0eff8e1aeaa5ca08997cfb60bc

Observation fe24aed3-eb8c-4c67-bb6e-84c061316485 · outbound

This paper cites Gemini Robotics 1.5: Pushing the Frontier of Generalist Robots with Advanced Embodied Reasoning, Thinking, and Motion Transfer.

Hy-Embodied-0.5-VLA: From Vision-Language-Action Models to a Real-World Robot Learning Stack Gemini Robotics 1.5: Pushing the Frontier of Generalist Robots with Advanced Embodied Reasoning, Thinking, and Motion Transfer

Reference 39

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source=pdf_text observed=2026-08-02T11:31:36.267157Z digest=sha256:d252875656632510c205a2d047ee3ad4c51122023179b0f26c1d3e7c7130e55d

Observation cc6265f1-7f8c-428f-a08a-5422de08cea0 · outbound

This paper cites Qwen3-VL Technical Report.

Hy-Embodied-0.5-VLA: From Vision-Language-Action Models to a Real-World Robot Learning Stack Qwen3-VL Technical Report

Reference 40

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source=pdf_text observed=2026-08-02T11:31:36.326667Z digest=sha256:98a678f764426ce2d58f5698c79c2ff2c276dd63ecd5c18c89532f04b12c1552

Observation f6fc61ea-e098-44a7-9d8c-524dbcc0b40a · outbound

This paper cites RoboBrain 2.5: Depth insight, time in mind.arXiv preprint arXiv:2601.14352, 2026.

Hy-Embodied-0.5-VLA: From Vision-Language-Action Models to a Real-World Robot Learning Stack RoboBrain 2.5: Depth insight, time in mind.arXiv preprint arXiv:2601.14352, 2026

Reference 41

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source=pdf_text observed=2026-08-02T11:31:36.442501Z digest=sha256:a3e747d015b9e014fe56ebda5b57ac123593f5c73122998459c7a14aaf7491e2

Observation 60e558cd-9a21-4dd7-9f0f-150bdf34a9b0 · outbound

This paper cites RynnBrain: Open embodied foundation models, 2026.

Hy-Embodied-0.5-VLA: From Vision-Language-Action Models to a Real-World Robot Learning Stack RynnBrain: Open embodied foundation models, 2026

Reference 42

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source=pdf_text observed=2026-08-02T11:31:36.559624Z digest=sha256:2fa69a1caf9973495493d9a882e73824eaafad45d827de024f2204868fcaba2e

Observation 200ca3cc-582b-4f5d-8c98-d8eab535b457 · outbound

This paper cites A Careful Examination of Large Behavior Models for Multitask Dexterous Manipulation.

Hy-Embodied-0.5-VLA: From Vision-Language-Action Models to a Real-World Robot Learning Stack A Careful Examination of Large Behavior Models for Multitask Dexterous Manipulation

Reference 43

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source=pdf_text observed=2026-08-02T11:31:36.636842Z digest=sha256:1b7643bb46f50f138718a322a2a3cabfab0a130fc256d35ea215982d2bebf889

Observation a542bd3d-a750-48a7-a3b7-b2bd971fb428 · outbound

This paper cites Open X-Embodiment: Robotic learning datasets and RT-X models.

Hy-Embodied-0.5-VLA: From Vision-Language-Action Models to a Real-World Robot Learning Stack Open X-Embodiment: Robotic learning datasets and RT-X models

Reference 44

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source=pdf_text observed=2026-08-02T11:31:36.716435Z digest=sha256:20333f209e88bd0bac3e0df8484f3e8eee7ed44fe7c60bf7b1efb29d4f2845b6

Observation f6eaf0b9-cd57-4fca-82c0-a6f430cfa052 · outbound

This paper cites DROID: A large-scale in-the-wild robot manipulation dataset.

Hy-Embodied-0.5-VLA: From Vision-Language-Action Models to a Real-World Robot Learning Stack DROID: A large-scale in-the-wild robot manipulation dataset

Reference 45

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source=pdf_text observed=2026-08-02T11:31:36.774413Z digest=sha256:8f77d11d32fa758f83ef4b81704855ed6201712e9cde494f2dee3d72329423d7

Observation 91f3249e-27b5-4265-b78b-67c721c1959d · outbound

This paper cites DexUMI:Usinghumanhandastheuniversalmanipulationinterfacefordexterous manipulation.arXiv preprint arXiv:2505.21864, 2025.

Hy-Embodied-0.5-VLA: From Vision-Language-Action Models to a Real-World Robot Learning Stack DexUMI:Usinghumanhandastheuniversalmanipulationinterfacefordexterous manipulation.arXiv preprint arXiv:2505.21864, 2025

Reference 46

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source=pdf_text observed=2026-08-02T11:31:36.946125Z digest=sha256:6dbef73ca589e9767beb4295548bf1f87fb0de5a242a96894d249d5773143ef8

Observation ca8baf1c-44ef-438c-a8ad-4af8d86f43ef · outbound

This paper cites EgoMI: Learning active vision and whole-body manipulation from egocentric human demonstrations.arXiv preprint arXiv:2511.00153, 2025.

Hy-Embodied-0.5-VLA: From Vision-Language-Action Models to a Real-World Robot Learning Stack EgoMI: Learning active vision and whole-body manipulation from egocentric human demonstrations.arXiv preprint arXiv:2511.00153, 2025

Reference 47

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source=pdf_text observed=2026-08-02T11:31:37.108065Z digest=sha256:8b961e7544b7fb9eece47456dbedd9581b6190eea745752dbbd611380340c256

Observation 1aeec58f-d749-479b-870d-8993e9bf925b · outbound

This paper cites HoMMI: Learning Whole-Body Mobile Manipulation from Human Demonstrations.

Hy-Embodied-0.5-VLA: From Vision-Language-Action Models to a Real-World Robot Learning Stack HoMMI: Learning Whole-Body Mobile Manipulation from Human Demonstrations

Reference 48

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source=pdf_text observed=2026-08-02T11:31:37.254234Z digest=sha256:34011bde926d88f4fbf5fceb1fbc4ba897d7803f9893e2511a428f30c4a64933

Observation 556c61e1-7d33-4302-8f83-f9953bbd9ff2 · outbound

This paper cites Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al.

Hy-Embodied-0.5-VLA: From Vision-Language-Action Models to a Real-World Robot Learning Stack Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al

Reference 49

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source=pdf_text observed=2026-08-02T11:31:37.402241Z digest=sha256:0aaa420d7f685d72cad49d96f005a3bf0a720107647028f50502609f95e5cbac

Observation b5524aa9-8e23-4178-82d9-5790001bdf09 · outbound

This paper cites Proximal Policy Optimization Algorithms.

Hy-Embodied-0.5-VLA: From Vision-Language-Action Models to a Real-World Robot Learning Stack Proximal Policy Optimization Algorithms

Reference 50

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source=pdf_text observed=2026-08-02T11:31:37.441697Z digest=sha256:e6179ae9b87134742f059e7cf4c56a2544ef2bd5f2a1684f39f105888bded896

Observation d17a8458-3dc5-4ad9-b322-4ecc3d3aebd1 · outbound

This paper cites Precise and dexterous robotic manipulationviahuman-in-the-loopreinforcementlearning.ScienceRobotics,10(105):eads5033, 2025.

Hy-Embodied-0.5-VLA: From Vision-Language-Action Models to a Real-World Robot Learning Stack Precise and dexterous robotic manipulationviahuman-in-the-loopreinforcementlearning.ScienceRobotics,10(105):eads5033, 2025

Reference 51

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source=pdf_text observed=2026-08-02T11:31:37.519446Z digest=sha256:d47571c981d3a3e47d26f68f70d159a64e8d5198366b6573aebff69b09ad1601

Observation 1b300e0e-48be-42ac-8f33-d35c435141b2 · outbound

This paper cites Manning, and Chelsea Finn.

Hy-Embodied-0.5-VLA: From Vision-Language-Action Models to a Real-World Robot Learning Stack Manning, and Chelsea Finn

Reference 52

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source=pdf_text observed=2026-08-02T11:31:37.608062Z digest=sha256:86793ec82d220036c5193e17c1d447f908726b1d82e9fb06775f8f1818ccc3be

Observation f7277805-4317-4e28-9e61-fc005ca76c40 · outbound

This paper cites GRAPE: Generalizing Robot Policy via Preference Alignment.

Hy-Embodied-0.5-VLA: From Vision-Language-Action Models to a Real-World Robot Learning Stack GRAPE: Generalizing Robot Policy via Preference Alignment

Reference 53

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source=pdf_text observed=2026-08-02T11:31:37.690666Z digest=sha256:92730d9080f7ff20a9a8788a917047d42cbdf02608fdb933a6f9c97f06087e8b

Observation 3df46ae2-a65b-4b19-a041-b8df0d4e7846 · outbound

This paper cites Real-time action chunking with large models.arXiv preprint arXiv:2503.07206, 2025.

Hy-Embodied-0.5-VLA: From Vision-Language-Action Models to a Real-World Robot Learning Stack Real-time action chunking with large models.arXiv preprint arXiv:2503.07206, 2025

Reference 54

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source=pdf_text observed=2026-08-02T11:31:37.762907Z digest=sha256:d0b0b83437703f3cd87519387066e7eba91415b93b42405a42a7ed4d1fe6af67

Observation 474e296e-5c38-4caa-aa2e-fa97999ce0f8 · outbound

This paper cites Training-time real-time chunking: Co-training high-frequency action refinement with policies, 2025.

Hy-Embodied-0.5-VLA: From Vision-Language-Action Models to a Real-World Robot Learning Stack Training-time real-time chunking: Co-training high-frequency action refinement with policies, 2025

Reference 55

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source=pdf_text observed=2026-08-02T11:31:37.806368Z digest=sha256:6f8572c162d53af1cf7ae32415f9d262f050b6c343c110bc376fd0f936934f9b

Observation 634cedbd-3fb7-4abf-bb05-de31eb4f9f47 · outbound

This paper cites VLASH: Real-Time VLAs via Future-State-Aware Asynchronous Inference.

Hy-Embodied-0.5-VLA: From Vision-Language-Action Models to a Real-World Robot Learning Stack VLASH: Real-Time VLAs via Future-State-Aware Asynchronous Inference

Reference 56

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source=pdf_text observed=2026-08-02T11:31:37.870540Z digest=sha256:18576ce8bb821fd94e877b19d191ea618c62b2a6b1b4bc90c9d5c77f6e8c926f

Observation 708b250d-3e07-4dbe-92b1-5cffa60e4259 · outbound

This paper cites ${\pi}_{0.7}$: a Steerable Generalist Robotic Foundation Model with Emergent Capabilities.

Hy-Embodied-0.5-VLA: From Vision-Language-Action Models to a Real-World Robot Learning Stack ${\pi}_{0.7}$: a Steerable Generalist Robotic Foundation Model with Emergent Capabilities

Reference 57

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source=pdf_text observed=2026-08-02T11:31:37.930748Z digest=sha256:c3deb568dcc7627f525886c8b245492ac69a8be3e8bce37bdc91c8c7ea6b35d3

Observation e5d90056-6070-42ff-b3a1-308ec18a7a1e · outbound

This paper cites It assumes that the UMI world frame and the robot chassis frame are related by a pure translation (identical orientation), which holds on Astribot S1.

Hy-Embodied-0.5-VLA: From Vision-Language-Action Models to a Real-World Robot Learning Stack It assumes that the UMI world frame and the robot chassis frame are related by a pure translation (identical orientation), which holds on Astribot S1

Reference 58

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source=pdf_text observed=2026-08-02T11:31:37.991471Z digest=sha256:bf7b79257a1aba49d2f30a5f450a3840c08fa390176e8f41f62463b025022583

Observation 174b1d95-e4b2-4e0b-9bd6-b3185770d12b · outbound

This paper cites We document this compatibility for completeness; our AstribotS1 results in §6.2 use the heuristic exclusively.

Hy-Embodied-0.5-VLA: From Vision-Language-Action Models to a Real-World Robot Learning Stack We document this compatibility for completeness; our AstribotS1 results in §6.2 use the heuristic exclusively

Reference 59

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source=pdf_text observed=2026-08-02T11:31:38.052183Z digest=sha256:7faa2cfd9d911d23dd954e38e42a6dcdaf73ec6b6e4e0c47f74a000d547e41de

Pith citing papers

Observation c3b573c9-d5bf-4023-8be1-018709071031 · inbound

ACE-Ego-0: Unifying Egocentric Human and Robotic Data for VLA Pretraining cites this paper.

ACE-Ego-0: Unifying Egocentric Human and Robotic Data for VLA Pretraining Hy-Embodied-0.5-VLA: From Vision-Language-Action Models to a Real-World Robot Learning Stack

Reference 61

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arxiv_id, observed 2026-07-21T02:21:39.644745Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-06-27T03:25:39.450667Z digest=sha256:acd60a12b0a5335dcb1ed12abfad50773bfbdced45a6265948fd7e3177af640f

Observation e6a6cb12-87f5-4a87-933f-14bf1666022c · inbound

RxBrain: Embodied Cognition Foundation Model with Joint Language-Visual Reasoning and Imagination cites this paper.

RxBrain: Embodied Cognition Foundation Model with Joint Language-Visual Reasoning and Imagination Hy-Embodied-0.5-VLA: From Vision-Language-Action Models to a Real-World Robot Learning Stack

Reference 55

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source=pdf_text observed=2026-08-02T03:16:53.174546Z digest=sha256:cf4a2500db3fd0035d3c279144244e591f0ade7e94d0cc1b2e4416ec375f1b58

Observation 20e0fc5f-94c4-43fc-ae96-5be679b4161b · inbound

Xiaomi-Robotics-1: Scaling Vision-Language-Action Models with over 100K Hours of Real-World Trajectories cites this paper.

Xiaomi-Robotics-1: Scaling Vision-Language-Action Models with over 100K Hours of Real-World Trajectories Hy-Embodied-0.5-VLA: From Vision-Language-Action Models to a Real-World Robot Learning Stack

Reference 85

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source=pdf_text observed=2026-08-02T00:04:10.301551Z digest=sha256:b3884a46731b6b4aa0ac6a38fbc2f7fb23689bb7d262f0fc4d4c5176e04c6275

Observation ff4dac7b-d6b9-4670-925d-1921f7e87ec0 · inbound

PhyAI: Real-Time Physical AI at the Edge, Scalable Rollouts in the Cloud cites this paper.

PhyAI: Real-Time Physical AI at the Edge, Scalable Rollouts in the Cloud Hy-Embodied-0.5-VLA: From Vision-Language-Action Models to a Real-World Robot Learning Stack

Reference 4

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source=arxiv_source observed=2026-08-05T14:52:35.364858Z digest=sha256:6ff633d75c110c0013a57c08f46d729b78248591838a7f615a10d30686593018

Observation 1b730ff3-fad8-42e1-b214-2063b86cdb27 · inbound

PhyAI: Real-Time Physical AI at the Edge, Scalable Rollouts in the Cloud cites this paper.

PhyAI: Real-Time Physical AI at the Edge, Scalable Rollouts in the Cloud Hy-Embodied-0.5-VLA: From Vision-Language-Action Models to a Real-World Robot Learning Stack

Reference 4

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source=arxiv_source observed=2026-08-15T14:52:22.168086Z digest=sha256:9bd6dd711fe06012fdfdcf3bbbfbded925f488b73c46973213f8b891ca702b34

Observation d6719aab-bbd0-4091-b0d5-750414dff7df · inbound

XPolicyLab: A Unified Standard and Open Ecosystem for Robot Policy Evaluation and Deployment cites this paper.

XPolicyLab: A Unified Standard and Open Ecosystem for Robot Policy Evaluation and Deployment Hy-Embodied-0.5-VLA: From Vision-Language-Action Models to a Real-World Robot Learning Stack

Reference 7

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source=pdf_text observed=2026-08-11T04:54:23.735057Z digest=sha256:9b1b33e5d34f116252aa128142e56a0c47785b8d985c7dce054a0fb09a9f01c3

Observation 8fd6cc3c-eaed-4eda-b6e2-8963623518f5 · inbound

XPolicyLab: A Unified Standard and Open Ecosystem for Robot Policy Evaluation and Deployment cites this paper.

XPolicyLab: A Unified Standard and Open Ecosystem for Robot Policy Evaluation and Deployment Hy-Embodied-0.5-VLA: From Vision-Language-Action Models to a Real-World Robot Learning Stack

Reference 7

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source=pdf_text observed=2026-08-14T04:17:47.263786Z digest=sha256:ff334004466f383bb41fa516a01d62277fc8a41a668fc241338718925e6511cf