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.
Specifically, the data collection phase is responsible for acquiring real-world data needed for training; the model design stage focuses on building multimodal models capable of handling vision, language, and action; continued pre-training and supervised fine-tuning ensure improved performance on specific tasks; RL post-training further optimizes model decisions through interaction with the environment; finally, the real-world deployment stage applies the trained model in robots for actual operations.
This system aims to enhance the adaptability and autonomous learning capabilities of robots in complex environments, pushing forward the advancement and application of robotic technology.
Blogger's Review: The introduction of HyVLA-0.5 marks a significant advancement in the field of robotic learning. Its integrative and systematic approach lays a foundation for future research, allowing researchers to more effectively explore and address challenges in robotic learning, making it a noteworthy development.