Andrea Tagliabue
Applied Scientist, Amazon Robotics
Cambridge, Massachusetts, United States
Summary
🤩
Rockstar🎓
Top SchoolAndrea Tagliabue is an applied scientist and aerial robotics specialist with a decade of experience building end-to-end autonomy for agile flying robots, now focused on Amazon Robotics after a PhD in Robotics from MIT. His work bridges low-level motion control, state estimation, perception, and multi-agent coordination, with notable contributions like lidar-inertial odometry used in the DARPA Subterranean Challenge and decentralized collaborative-transport strategies. He has driven data- and compute-efficient imitation learning for vision-based agile control—achieving up to 10x improvements via MPC-guided augmentation—and pioneered NeRF-based augmentation to shrink training needs for visual policies. Comfortable moving between simulation, theory, and field deployment, Andrea blends robust control, uncertainty-aware planning, and exploratory LLM work for low-level resilience, reflecting a rare combination of academic rigor and hands-on systems engineering.
10 years of coding experience
7 years of employment as a software developer
Master Thesis Motion Planning and Control, Master Thesis Motion Planning and Control at University of California, BerkeleyDoctor of Philosophy - PhD Robotics, Doctor of Philosophy - PhD Robotics at Massachusetts Institute of TechnologyUPC Universitat Politècnica de CatalunyaB.Sc. Mechatronics Robotics and Automation Engineering, B.Sc. Mechatronics Robotics and Automation Engineering at Politecnico di MilanoM.Sc. Robotics Systems and Control, M.Sc. Robotics Systems and Control at ETH Zürich
English, Spanish, Italian, Catalan
Github Skills (58)
simulator10
ignition10
yarp10
gazebo-simulator10
image-processing9
gazebosim9
robotics9
simulation9
robot-simulator9
lidar9
slam9
ros9
robotics-simulation9
q-learning8
computer-vision8
Programming languages (5)
Github contributions (5)
andretag/andretag.github.io
Feb 2018 - Jun 2024
Contributions:2 PRs, 39 pushes, 1 branch in 6 years 4 months
andretag/gp
Jul 2020 - Aug 2020
A lightweight C++ library for Gaussian processes (GPs).
Contributions:4 pushes in 20 days
cppc-librarygaussiangpsgaussian-processes
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