Anton Milan
Papers
9
Total Citations
419
H-Index
7
About
Anton Milan is a leading roboticist whose work bridges computer vision and autonomous manipulation, with a focus on enabling robots to perceive and interact with cluttered, unstructured environments. His most impactful contributions center on deep-learning-based perception for robotic grasping, particularly semantic segmentation and object detection from RGB-D data. Milan’s research directly addresses the challenge of handling partially occluded objects, shiny and transparent surfaces, and unseen object categories—critical for real-world applications like warehouse automation. He is best known as the lead designer of Cartman, the low-cost Cartesian manipulator that won first place in the 2017 Amazon Robotics Challenge, a feat documented in papers with over 140 citations. His work on semantic segmentation from limited training data (52 citations) further advanced robotic perception under data constraints. Milan also contributed to the MOTChallenge benchmark for multiple object tracking, underscoring his broader impact in computer vision. With over 400 total citations, his research has shaped both academic understanding and practical deployment of autonomous pick-and-place systems, making him a key figure in modern robotics.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3Semantic Segmentation from Limited Training Data52 citations · 2018
- 4
- 5
- 6MOTChallenge: A Benchmark for Single-Camera Multiple Target Tracking10 citations · 2020
- 7Mechanical Design of a Cartesian Manipulator for Warehouse Pick and Place10 citations · 2017
- 8Semantic Segmentation from Limited Training Data5 citations · 2017
- 9Energy Minimization for Multiple Object Tracking4 citations · 2014