10 citations · 11 across the 8 of their papers we have counts for
11 papers
CGD: Constraint-Guided Diffusion Policies for UAV Trajectory Planning
Kota Kondo, Andrea Tagliabue, Xiaoyi Cai +4
Traditional optimization-based planners, while effective, suffer from high computational costs, resulting in slow trajectory generation. A successful strategy to reduce computation…
Output Feedback Tube MPC-Guided Data Augmentation for Robust, Efficient Sensorimotor Policy Learning
Andrea Tagliabue, Jonathan P. How
Imitation learning (IL) can generate computationally efficient sensorimotor policies from demonstrations provided by computationally expensive model-based sensing and control algor…
Robust, High-Rate Trajectory Tracking on Insect-Scale Soft-Actuated Aerial Robots with Deep-Learned Tube MPC
Andrea Tagliabue, Yi-Hsuan Hsiao, Urban Fasel +4
Accurate and agile trajectory tracking in sub-gram Micro Aerial Vehicles (MAVs) is challenging, as the small scale of the robot induces large model uncertainties, demanding robust…
Demonstration-Efficient Guided Policy Search via Imitation of Robust Tube MPC
Andrea Tagliabue, Dong-Ki Kim, Michael Everett +1
We propose a demonstration-efficient strategy to compress a computationally expensive Model Predictive Controller (MPC) into a more computationally efficient representation based o…
Airflow-Inertial Odometry for Resilient State Estimation on Multirotors
Andrea Tagliabue, Jonathan P. How
We present a dead reckoning strategy for increased resilience to position estimation failures on multirotors, using only data from a low-cost IMU and novel, bio-inspired airflow se…
NeBula: Quest for Robotic Autonomy in Challenging Environments; TEAM CoSTAR at the DARPA Subterranean Challenge
Ali Agha, Kyohei Otsu, Benjamin Morrell +69
This paper presents and discusses algorithms, hardware, and software architecture developed by the TEAM CoSTAR (Collaborative SubTerranean Autonomous Robots), competing in the DARP…