Awesome VLM-based VLA for Robotic Manipulation
April 3, 2026 ยท View on GitHub
Awesome VLM-based VLA for Robotic Manipulation
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๐ฅ Large VLM-based Vision-Language-Action (VLA) models have recently emerged as a transformative paradigm for robotic manipulation by tightly coupling perception, language understanding, and action generation. Built upon large Vision-Language Models (VLMs), they enable robots to interpret natural language instructions, perceive complex environments, and perform diverse manipulation tasks with strong generalization.
๐ We present the first systematic survey on large VLM-based VLA models for robotic manipulation. This repository serves as the companion resource to our survey: "Large VLM-based Vision-Language-Action Models for Robotic Manipulation: A Survey", and includes all the research papers, benchmarks, and resources reviewed in the paper, organized for easy access and reference.
๐ We will keep updating this repository with newly published works to reflect the latest progress in the field.
Table of Contents
- ๐ค Awesome VLA for Robotic Manipulation
Monolithic Models
Single-System
Dual-System
Hierarchical Models
Planner Only
Planner + Policy
Other Advanced Field
Reinforcement Learning-based Methods
Training-Free Methods
Learning from Human Videos
World Model-based VLA
Datasets and Benchmarks
Real-world Robot Datasets
Simulation Environments and Benchmarks
Human Behavior Datasets
Embodied Datasets and Benchmarks
Citation
If you find this survey helpful for your research or applications, please consider citing it using the following BibTeX entry:
@misc{shao2025largevlmbasedvisionlanguageactionmodels,
title={Large VLM-based Vision-Language-Action Models for Robotic Manipulation: A Survey},
author={Rui Shao and Wei Li and Lingsen Zhang and Renshan Zhang and Zhiyang Liu and Ran Chen and Liqiang Nie},
year={2025},
eprint={2508.13073},
archivePrefix={arXiv},
primaryClass={cs.RO},
url={https://arxiv.org/abs/2508.13073},
}
Contact Us
For any questions or suggestions, please feel free to contact us at:
Email: shaorui@hit.edu.cn and liwei2024@stu.hit.edu.cn