Hy-Embodied-0.5-VLA: From Vision-Language-Action Models to a Real-World Robot Learning Stack
Abstract
HyVLA-0.5 is an end-to-end robotic learning system that integrates data collection, model design, pre-training, fine-tuning, and reinforcement learning for real-world deployment.
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.
Community
This is an automated message from the Librarian Bot. I found the following papers similar to this paper.
The following papers were recommended by the Semantic Scholar API
- GEAR-VLA: Learning Geometry-Aware Action Representations for Generalizable Robotic Manipulation (2026)
- ELAN4D: Embodiment-Centric 4D Supervision for Vision-Language-Action Models via Plug-and-Play Adaptation (2026)
- MotuBrain: An Advanced World Action Model for Robot Control (2026)
- Qwen-VLA: Unifying Vision-Language-Action Modeling across Tasks, Environments, and Robot Embodiments (2026)
- Wall-OSS-0.5 Technical Report (2026)
- PiL-World: A Chunk-Wise World Model for VLA Policy-in-the-Loop Evaluation (2026)
- EvoScene-VLA: Evolving Scene Beliefs Inside the Action Decoder for Chunked Robot Control (2026)
Please give a thumbs up to this comment if you found it helpful!
If you want recommendations for any Paper on Hugging Face checkout this Space
You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: @librarian-bot recommend
Get this paper in your agent:
hf papers read 2606.14409 Don't have the latest CLI?
curl -LsSf https://hf.co/cli/install.sh | bash Models citing this paper 2
Datasets citing this paper 1
Spaces citing this paper 0
No Space linking this paper