Papers
16
Total Citations
273
H-Index
7
About
Andrea Tagliabue is a robotics researcher whose work spans autonomous systems, aerial robotics, and intelligent control, with particular emphasis on enabling robust robot behavior in challenging and unstructured environments. He is perhaps best known for his contributions to the DARPA Subterranean Challenge as a member of TEAM CoSTAR, whose NeBula autonomy framework won Phase II of the competition. These landmark papers—accumulating over 150 citations combined—detail sophisticated algorithms for multi-robot collaboration, perception, and navigation in GPS-denied underground environments. Beyond subterranean robotics, Tagliabue has made significant contributions to hybrid aerial-ground mobility, introducing the Rollocopter concept for extreme terrain traversal, and to planetary exploration through the Shapeshifter platform designed for Saturn's moon Titan. A recurring theme in his research is bridging computationally expensive model predictive control (MPC) with efficient deep learning through imitation learning and tube-guided data augmentation, producing policies deployable on resource-constrained platforms including insect-scale micro aerial vehicles. More recently, he has explored the integration of large language models for resilient autonomous aerial systems. Tagliabue's diverse portfolio reflects a commitment to pushing the boundaries of robot autonomy across scales and environments, from underground tunnels to outer planetary bodies.
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