Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-02T11:31:38.052183Z
Paper Citation Record · LEDGER
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.
Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-02T11:31:38.052183Z
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-15T14:52:22.168086Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-03T17:58:47.581063Z
59 of 59 outbound references displayed
External citation measurements
No source-named external measurement is stored.
Observation 94afc17a-f003-4b04-a8ee-fb99ac0741ac · outbound
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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Observation 45c6e1ed-1768-497c-911e-43286859d14b · outbound
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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Observation 7874eeb9-fb50-463a-ab99-829a8de419db · outbound
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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Observation 0524bf3f-8e8e-4d1d-b2d8-e09259cf5ef4 · outbound
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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Observation f46377fb-4004-4e91-9070-1c3c90f562f2 · outbound
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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Observation fc79ab49-c0f3-432e-84db-1be1c76d50e6 · outbound
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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Observation 6361d3e5-9029-47ca-83ae-b6fa77a0dee4 · outbound
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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Observation eedd00fd-10fd-4e3c-9bd6-3361a0385231 · outbound
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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Observation b0a28147-1a24-4c47-aa17-1de436a5d9a1 · outbound
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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Observation dfd02d4c-9903-4d68-b27d-b050b2b5d047 · outbound
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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Observation 7f4a8db4-ee1c-494d-bcfb-9939e13abb43 · outbound
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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Observation 95fc349b-3599-4e89-8c9c-958dede8c1f9 · outbound
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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Observation 50ebb30d-8d3d-4280-918c-7e8035ed9403 · outbound
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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Unavailable: canonical work link unavailable.
Observation 9e0cae91-1e55-4460-a8ef-0c6865aff714 · outbound
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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Observation 30bada3c-3e97-493e-9f91-a54c9998d64a · outbound
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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Observation 7b4ab863-bfe2-4ead-8a05-6b3f17ea3036 · outbound
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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Observation 870da128-f1a9-422e-8b50-cac0d040177f · outbound
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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Observation 171f1d79-6b59-477e-a1a0-3e6a776128c1 · outbound
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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Observation 3329dd95-3ae2-4c66-811f-3187e50d2f02 · outbound
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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Observation 7369cda2-6989-4374-a897-c5b7be7fd152 · outbound
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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Observation 24cd364b-b6ec-45c8-bb8c-902a7b967cc2 · outbound
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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Observation 2e642bcc-965d-4763-ab88-c6f59406c859 · outbound
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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Observation 97c95388-0135-4ffe-9298-0db600ef27b8 · outbound
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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Observation eec9ecd5-c9cb-47d0-b41a-393c33bba930 · outbound
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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Unavailable: canonical work link unavailable.
Observation 1e1020fe-9899-4b63-bcf8-dfbf07649fcc · outbound
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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Observation 1b4834d9-e0e3-48b2-a26b-8ece65518e48 · outbound
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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Unavailable: canonical work link unavailable.
Observation 1e28eb41-657e-4f9d-8e96-2ad7cfefdada · outbound
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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Unavailable: canonical work link unavailable.
Observation a8382ff1-fa55-4bda-82f6-bdb7c80b0594 · outbound
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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Observation e8cf599b-b197-4736-824e-a9ece5b996b8 · outbound
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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Observation 5c1af7cf-3870-4a6b-8459-7321172c371c · outbound
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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Observation e541f357-c7fc-435d-9000-74da34a65c78 · outbound
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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Observation 575ca4fb-98f2-4422-aa37-07b761179b1d · outbound
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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Observation 9f7fdae9-34af-4925-a43f-829cc07a36eb · outbound
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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Observation 251a2b84-2d2a-4e4b-80ab-a02fccad2719 · outbound
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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Observation bc205fed-e7eb-4f04-a25d-67c79a1e68a7 · outbound
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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Observation 4751e36a-1746-448c-be14-77ab8b48efab · outbound
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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Observation 675391f3-76ab-418c-a189-2137423f2084 · outbound
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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Observation da60f461-7009-4121-a428-50317619ac58 · outbound
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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Observation fe24aed3-eb8c-4c67-bb6e-84c061316485 · outbound
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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Observation cc6265f1-7f8c-428f-a08a-5422de08cea0 · outbound
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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Observation f6fc61ea-e098-44a7-9d8c-524dbcc0b40a · outbound
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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Observation 60e558cd-9a21-4dd7-9f0f-150bdf34a9b0 · outbound
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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Observation 200ca3cc-582b-4f5d-8c98-d8eab535b457 · outbound
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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Observation a542bd3d-a750-48a7-a3b7-b2bd971fb428 · outbound
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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Observation f6eaf0b9-cd57-4fca-82c0-a6f430cfa052 · outbound
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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Observation 91f3249e-27b5-4265-b78b-67c721c1959d · outbound
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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Observation ca8baf1c-44ef-438c-a8ad-4af8d86f43ef · outbound
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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Observation 1aeec58f-d749-479b-870d-8993e9bf925b · outbound
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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Observation 556c61e1-7d33-4302-8f83-f9953bbd9ff2 · outbound
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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Observation b5524aa9-8e23-4178-82d9-5790001bdf09 · outbound
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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Observation d17a8458-3dc5-4ad9-b322-4ecc3d3aebd1 · outbound
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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Observation 1b300e0e-48be-42ac-8f33-d35c435141b2 · outbound
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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Observation f7277805-4317-4e28-9e61-fc005ca76c40 · outbound
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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Observation 3df46ae2-a65b-4b19-a041-b8df0d4e7846 · outbound
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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Observation 474e296e-5c38-4caa-aa2e-fa97999ce0f8 · outbound
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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Observation 634cedbd-3fb7-4abf-bb05-de31eb4f9f47 · outbound
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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Observation 708b250d-3e07-4dbe-92b1-5cffa60e4259 · outbound
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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Observation e5d90056-6070-42ff-b3a1-308ec18a7a1e · outbound
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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Observation 174b1d95-e4b2-4e0b-9bd6-b3185770d12b · outbound
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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Observation c3b573c9-d5bf-4023-8be1-018709071031 · inbound
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
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.
Observation e6a6cb12-87f5-4a87-933f-14bf1666022c · inbound
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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Observation 20e0fc5f-94c4-43fc-ae96-5be679b4161b · inbound
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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Observation ff4dac7b-d6b9-4670-925d-1921f7e87ec0 · inbound
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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Observation 1b730ff3-fad8-42e1-b214-2063b86cdb27 · inbound
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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Observation d6719aab-bbd0-4091-b0d5-750414dff7df · inbound
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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Observation 8fd6cc3c-eaed-4eda-b6e2-8963623518f5 · inbound
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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