Max Robotics — Global Service Robot Directory + US Certification

About

Cagla Acun is a rising researcher at the forefront of trustworthy and transparent robotic autonomy. Her work centers on a critical challenge in human-robot collaboration: predicting and explaining robotic grasp failures. Rather than simply building accurate machine learning models, Acun’s major contribution lies in pioneering *pre-hoc* explainability frameworks—designing models that are inherently interpretable from the start, rather than relying on opaque post-hoc explanations. Her 2024 paper on this topic has already garnered 4 citations, signaling its importance in the field. Acun’s research systematically compares explainability methods, optimizes local (instance-level) transparency using advanced divergence metrics, and leverages deep learning sequence models for failure prediction. With five citations on her 2024 work alone, her impact is growing rapidly. Notably, her 2025 comparative analysis and optimization study each hold 3 citations, reflecting a sustained, focused trajectory. For students and researchers, Acun’s work is a compelling blueprint for building robotic systems that are not only capable but also accountable—a vital step toward practical, trusted autonomy.