Ivan Papusha
Papers
1
Total Citations
10
H-Index
1
About
Ivan Papusha’s research lies at the intersection of optimal control, formal methods, and robotics, with a focus on enabling autonomous systems to make provably safe and efficient decisions under complex constraints. His most-cited work, “Sampling-based Approximate Optimal Control Under Temporal Logic Constraints” (2017, 10 citations), introduces a novel approach that combines sampling-based motion planning with co-safe linear temporal logic specifications. By translating high-level temporal logic requirements into a deterministic finite automaton, Papusha develops a method that allows continuous-time, continuous-state systems—including nonlinear dynamics—to satisfy complex mission objectives while optimizing performance. This work bridges the gap between formal verification and practical control, offering a scalable framework for tasks like autonomous navigation and multi-agent coordination. Papusha’s contributions are particularly impactful for researchers working on safe autonomy, where ensuring that a robot’s behavior adheres to logical specifications is critical. His approach has been cited in subsequent studies on temporal logic-based planning and sampling algorithms, highlighting its influence in advancing the field of control under logical constraints.
Research Focus
Key Achievements
Top Papers
- 1Sampling-based Approximate Optimal Control Under Temporal Logic Constraints10 citations · 2017