Michael Andrev
Papers
1
Total Citations
8
H-Index
1
About
Michael Andrev is a rising figure at the intersection of robotics and artificial intelligence, with a primary focus on safe, long-horizon task planning. His most influential work, "Safety Aware Task Planning via Large Language Models in Robotics" (2025), tackles a critical bottleneck in deploying LLMs for real-world robotic systems: the inherent tension between task completion and risk mitigation. By proposing frameworks that integrate safety constraints directly into LLM-driven planning, Andrev addresses a fundamental gap in the field—ensuring that autonomous agents not only reason about complex workflows but also prioritize hazard avoidance. This contribution has already garnered 8 citations, signaling its timely impact as the robotics community grapples with the safety implications of generative AI. His research sits at the nexus of machine learning, control theory, and human-robot interaction, offering practical pathways for deploying LLMs in manufacturing, healthcare, and service robotics. Andrev’s work is particularly notable for its emphasis on interpretability and verifiability, making his safety-aware planning methods both rigorous and deployable. As the demand for trustworthy autonomous systems grows, Andrev’s contributions are poised to shape how next-generation robots reason, act, and collaborate safely alongside humans.
Research Focus
Key Achievements
Top Papers
- 1Safety Aware Task Planning via Large Language Models in Robotics8 citations · 2025