AutMn Lab

Thrilled and honored to share that the U.S. National Science Foundation () has awarded a $600,000 grant for our project (Award #2540740):

"Formalized Autonomous Planning from High-Level Task Descriptions in Natural Language."

I'm delighted to be serving as Co-PI on this project alongside PI Dr. Hongsheng He.

This project aims to advance the integration of Large Language Models (LLMs) with autonomous planning by transforming high-level natural language task descriptions into formal, reliable plans that autonomous systems can execute safely and effectively.

My sincere thanks to the U.S. National Science Foundation () for supporting this research, and to our NSF Program Director, Dr. Erion Plaku, for his guidance and support.

Looking forward to the exciting research ahead!

[ U.S. National Science Foundation, NSF, NSF Award 2540740, Large Language Models, LLMs, Autonomous Planning, Natural Language Understanding, Formal Methods, Trustworthy AI, Robotics ]

Thrilled & honored to receive the NSF REU student funding under my ongoing NSF award ( #2525849)!

If you're a CS undergraduate at UA interested in designing safe controllers for robotic systems, with opportunities to work directly on real hardware platforms, feel free to reach out. Students with good mathematical maturity and programming experience are encouraged to apply; prior exposure to hardware platforms is a plus.

Please send your CV and transcript via email (Subject: REU Opportunity).

Details: bineet.cs.ua.edu

Lab Details: autmn.ua.edu

[ NSF REU, Undergraduate Research, University of Alabama, Robotics Research, Safe Autonomous Systems, Computer Science Students, Robotics Hardware, Formal Methods, AutMn Lab ]

A new look for AutMn.
Same mission—building systems you can trust.
Design. Verify. Ensure safety.

Ever see AI agents learn to hold the door for each other? A new research teaches robot teams to master complex, multi-step sequences and work together like a dream!

Comment to know more.

Paper: Yalcinkaya, B., et al. (2025). Automata-Conditioned Cooperative Multi-Agent Reinforcement Learning.

Keywords: MARL, AI Research, Reinforcement Learning, Robotics, Computer Science

A new look for AutMn.

Same mission—building systems you can trust.
Design. Verify. Ensure safety.