Andrea Tagliabue

I received a Ph.D. in Robotics and Machine Learning (Autonomous Systems) from LIDS at MIT in 2024, with a thesis on the topic of Efficient Imitation Learning for Robust, Adaptive, Vision-based Agile Control Under Uncertainty. My research interests include foundation models for sensing/planning, learning-based perception, imitation/reinforcement learning, data augmentation, learning-based adaptive/robust model predictive control.

Before joining MIT, I spent one year as a robotics engineer affiliate at the NASA Jet Propulsion Laboratory and at Caltech, contributing to the JPL/Caltech/MIT team for the Darpa Subterranean Challenge. My focus was on state estimation, developing a lidar-inertial odometry algorithm.

I completed a Master's degree in Robotics in 2018 at ETH Zürich, where I worked on multi-agent communication-less aerial manipulation as a research assistant in the Autonomous Systems Lab , and I was a member of the ETH team for the MBZ Int. Robotic Challenge. I was also visiting researcher at the HiPerLab at U.C. Berkeley, working on energy-efficient motion planning.

I received a Bachelor with honours at Politecnico di Milano in 2015 studying Automation Engineering (EECS).

[atagliab@mit.edu | linkedin | google scholar]

Preprint
	CGD: Constraint-Guided Diffusion Policies for UAV Trajectory Planning Kota Kondo, Andrea Tagliabue, Xiaoyi Cai, Claudius Tewari, Olivia Garcia, Marcos Espitia-Alvarez, Jonathan P How ArXiv preprint (under review for the Control and Decision Conference) [paper]
Journal
	Efficient Deep Learning of Robust Policies from MPC using Imitation and Tube-Guided Data Augmentation Andrea Tagliabue, Jonathan P. How ArXiv preprint (accepted on May 30th 2024 for Transactions on Robotics) [paper \| video]
	Tube-NeRF: Efficient Imitation Learning of Visuomotor Policies from MPC using Tube-Guided Data Augmentation and NeRFs Andrea Tagliabue, Jonathan P. How Robotics and Automation Letters RA-L 2024 [paper \| video]
	Nebula: Quest for robotic autonomy in challenging environments; team costar at the darpa subterranean challenge Ali Agha, Kyohei Otsu, Benjamin Morrell, David D Fan, Rohan Thakker, Angel Santamaria-Navarro, Sung-Kyun Kim, Amanda Bouman, Xianmei Lei, Jeffrey Edlund, Muhammad Fadhil Ginting, Kamak Ebadi, Matthew Anderson, Torkom Pailevanian, Edward Terry, Michael Wolf, Andrea Tagliabue, et Al, 2022 Journal of Field Robotics [paper \| videos \| project webpage]
	Robust Collaborative Object Transportation Using Multiple MAVs Andrea Tagliabue, Mina Kamel, Roland Siegwart, and Juan Nieto, The International Journal of Robotics Research 38 (9), 1020-1044 [paper \| video \| video teaser ]
	Model-free online motion adaptation for energy efficient flights of multicopters Xiangyu Wu, Jun Zeng, Andrea Tagliabue, Mark W Mueller, 2022 IEEE Access [paper \| video ]
Conference
	Efficient Deep Learning of Robust, Adaptive Policies using Tube MPC-Guided Data Augmentation Tong Zhao, Andrea Tagliabue*, Jonathan P. How, IROS*, 2023 [paper \| video]
	REAL: Resilience and Adaptation using Large Language Models on Autonomous Aerial Robots Andrea Tagliabue, Kota Kondo, Tong Zhao, Mason Peterson, Claudius T Tewari, Jonathan P. How, Workshop on Language and Robot Learning, CoRL 2023 [paper]
	Robust, high-rate trajectory tracking on insect-scale soft-actuated aerial robots with deep-learned tube MPC Andrea Tagliabue, Yi-Hsuan Hsiao, Urban Fasel, J Nathan Kutz, Steven L Brunton, YuFeng Chen, Jonathan P. How, ICRA, 2023 (Awarded Finalist for the Best Paper in Dynamics and Control) [paper \| video]
	Output Feedback Tube MPC-Guided Data Augmentation for Robust, Efficient Sensorimotor Policy Learning Andrea Tagliabue, Jonathan P. How IROS, 2023 [paper \| code]
	Demonstration-Efficient Guided Policy Search via Imitation of Robust Tube MPC Andrea Tagliabue, Dong-Ki Kim, Michael Everett, Jonathan P. How ICRA, 2022 [paper \| video]
	Lion: Lidar-inertial observability-aware navigator for vision-denied environments Andrea Tagliabue, Jesus Tordesillas, Xiaoyi Cai, Angel Santamaria-Navarro, Jonathan P. How, Luca Carlone, Ali-akbar Agha-mohammadi International Symposium on Experimental Robotics, 2021 2022 (1st Place Urban Circuit, Darpa Subterranean Challenge)* ( equally contributed)* [paper \| video]
	Airflow-Inertial Odometry for Resilient State Estimation on Multirotors Andrea Tagliabue, Jonathan P. How ICRA, 2021 [paper]
	Autonomous MAV Landing on a Moving Platform with Estimation of Unknown Turbulent Wind Conditions Aleix Paris, Andrea Tagliabue, Jonathan P. How AIAA Scitech 2021 Forum [paper]
	Touch the Wind: Simultaneous Airflow, Drag and Interaction Sensing on a Multirotor Andrea Tagliabue, Aleix Paris, Suhan Kim, Regan Kubicek, Sarah Bergbreiter, Jonathan P. How IROS, 2020 ( equally contributed)* [paper \| video]
	Model-free Online Motion Adaptation for Optimal Range and Endurance of Multicopters Andrea Tagliabue, Xiangyu Wu, Mark W. Mueller ICRA, 2019 [paper]
	Shapeshifter: A Multi-Agent, Multi-Modal Robotic Platform for the Exploration of Titan Andrea Tagliabue, Stephanie Schneider, Marco Pavone, Ali-akbar Agha-mohammad 2020 IEEE Aerospace Conference [news \| paper \| short NASA video \| long video]
	Rollocopter: An Energy-Aware Hybrid Aerial-Ground Mobility for Extreme Terrains Sahand Sabet, Ali-Akbar Agha-Mohammadi, Andrea Tagliabue, D Sawyer Elliott, Parviz E Nikravesh 2019 IEEE Aerospace Conference [paper]
	Collaborative Transportation Using MAVs via Passive Force Control Andrea Tagliabue, Mina Kamel, Roland Siegwart, and Juan Nieto ICRA, 2017 [paper \| video]

Template credits: Jon Barron.

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Conference