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About

Can Palaz is a leading researcher in the integration of high-level symbolic reasoning with low-level geometric control for autonomous robotic manipulation. His work fundamentally addresses the "symbol grounding" problem, bridging the gap between abstract task planning and continuous motion execution. Palaz’s most influential contribution, a formal framework combining causal reasoning with geometric and motion planning (126 citations), enables robots to reason about causality while simultaneously computing feasible physical trajectories. This bilateral interaction between task and motion planning allows for more robust and adaptable robotic behavior. His research is prominently demonstrated through the classic Tower of Hanoi challenge, which he established as a standardized robotics benchmark for evaluating the seamless integration of high-level reasoning and low-level control. Palaz also developed logic-based frameworks using action description languages like C+ to translate discrete task plans into continuous, executable trajectories. By embedding geometric constraints directly into causal reasoning, his work has laid critical groundwork for execution monitoring systems that ensure robots can adapt their plans in real-time, marking a significant step toward truly autonomous, reasoning-driven robotic systems.