aiqbench takeMachine Learning

Hy-Embodied-0.5-VLA represents a significant advancement in robotics by integrating the full robot learning stack from data collection to real-world deployment within an end-to-end system. This research bridges vision-language-action models with practical implementation, offering critical insights for developers focused on embodied AI and autonomous agent architectures. The work underscores the evolving complexity of training large-scale robotic systems that can generalize across diverse physical tasks.

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

arXivJuly 21, 20261 min read

arXiv:2606.14409v2 Announce Type: replace-cross Abstract: In this report, we present Hy-Embodied-0.5-VLA, abbreviated as HyVLA-0.5, an end-to-end system that spans the full robot learning stack: data collection, model design, continued pre-training and supervised fine-tuning, RL post-training, and real-world deployment. Each component serves a distinct role in this stack.

Abstract:In this report, we present Hy-Embodied-0.5-VLA, abbreviated as HyVLA-0.5, an end-to-end system that spans the full robot learning stack: data collection, model design, continued pre-training and supervised fine-tuning, RL post-training, and real-world deployment. Each component serves a distinct role in this stack.

Robotics (cs.RO); Artificial Intelligence (cs.AI)

arXiv:2606.14409 [cs.RO]

(or arXiv:2606.14409v2 [cs.RO] for this version)

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