Awesome Agentic Robots
Official companion repository for
Towards Agentic Robots: Model, Data, Environment, and Harness
Overview · Browse by domain · Papers · Contributing · Cite
From task-specific policies to VLA models, world models, and agentic robot systems.
Overview
Agentic robots use foundation models to guide robot execution and learning. We organize this work around Model, Data, Environment, and Harness, connecting model decisions, data pipelines, environments, and the orchestration of robot capabilities.
Four functional domains and seventeen non-exclusive categories organize the literature on agentic robots.
Taxonomy
| Domain | Categories |
|---|---|
| Model | Training · Perception · Planning · Robot Control · Adaptation |
| Data | Collection · Annotation · Refinement |
| Environment | Reconstruction · Task Generation · Benchmarking |
| Harness | Orchestration · Memory · Monitoring and Recovery · Skills · Infrastructure · Layered Systems |
Papers
Model
Training · Perception · Planning · Robot Control · Adaptation
Model · Training
Model · Perception
Model · Planning
Model · Robot Control
Model · Adaptation
Data
Collection · Annotation · Refinement
Data · Collection
Data · Annotation
Data · Refinement
Environment
Reconstruction · Task Generation · Benchmarking
Environment · Reconstruction
Environment · Task Generation
Environment · Benchmarking
Harness
Orchestration · Memory · Monitoring and Recovery · Skills · Infrastructure · Layered Systems
Harness · Orchestration
Harness · Memory
Harness · Monitoring and Recovery
Harness · Skills
Harness · Infrastructure
Harness · Layered Systems
Contributing
Paper suggestions are welcome. See CONTRIBUTING.md for the scope and submission details.
Star History
Citation
@article{qin2026towards, title={Towards Agentic Robots: Model, Data, Environment, and Harness}, author={Chonghan Qin and Dongming Wu and Kaituo Feng and Jiaxu Wang and Xinlei Yu and Peiwen Sun and Yijia Fan and Xilin He and Bingao Chen and Weitong Lian and Ryan Ji and Wenqi Ren and Xiaobin Hu and Dahua Lin and Yu-Gang Jiang and Xiangyu Yue}, year={2026}, journal={arXiv preprint arXiv:2610.20442}, }





