Apostolos Modas
Papers
1
Total Citations
41
H-Index
1
About
Apostolos Modas is a leading researcher in the intersection of computer vision and robotic manipulation, with a particular focus on enabling robots to seamlessly interact with unknown objects in human-centric environments. His work addresses the critical challenge of real-time physical property estimation—such as 3D pose, dimensions, and weight—of objects being handed over by humans, a key bottleneck for safe and accurate human-to-robot handovers. His most cited paper, "Benchmark for Human-to-Robot Handovers of Unseen Containers With Unknown Filling" (2020, 41 citations), introduced a rigorous framework for evaluating vision-based control in dynamic, real-world scenarios, advancing the field's ability to handle uncertainty. Modas’s contributions have direct implications for assistive robotics, manufacturing, and logistics, where robots must adapt to novel objects without prior training. His work is widely recognized for bridging the gap between theoretical perception models and practical robotic systems, earning him a reputation for impactful, application-driven research.
Research Focus
Key Achievements
Top Papers
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