@conference {cuniato2023design,
	title = {Design and Control of a Micro Overactuated Aerial Robot with an Origami Delta Manipulator},
	booktitle = {2023 IEEE International Conference on Robotics and Automation (ICRA)},
	year = {2023},
	month = {2023/05},
	author = {Eugenio Cuniato and Christian Geckeler and Maximilian Brunner and Dario Str{\"u}bin and Elia B{\"a}hler and Fabian Ospelt and Marco Tognon and Stefano Mintchev and Roland Siegwart}
}
@conference {cuniato2023learning,
	title = {Learning to Open Doors with an Aerial Manipulator},
	booktitle = {2023 IEEE/RSJ International Conference on Intelligent Robots and Systems},
	year = {2023},
	month = {2023/10},
	author = {Eugenio Cuniato and Ismail Geles and Weixuan Zhang and Olov Andersson and Marco Tognon and Roland Siegwart}
}
@conference {126,
	title = {Active Model Learning using Informative Trajectories for Improved Closed-Loop Control on Real Robots},
	booktitle = {2021 IEEE Int. Conf. on Robotics and Automation},
	year = {2021},
	month = {2021/04},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Xi{\textquoteright}an, China},
	abstract = {<p style="text-align: justify;">Model-based controllers on real robots require accurate knowledge of the system dynamics to perform optimally. For complex dynamics, first-principles modeling is not sufficiently precise, and data-driven approaches can be leveraged to learn a statistical model from real experiments. However, the efficient and effective data collection for such a data-driven system on real robots is still an open challenge. This paper introduces an optimization problem formulation to find an informative trajectory that allows for efficient data collection and model learning. We present a sampling-based method that computes an approximation of the trajectory that minimizes the prediction uncertainty of the dynamics model. This trajectory is then executed, collecting the data to update the learned model. In experiments we demonstrate the capabilities of our proposed framework when applied to a complex omnidirectional flying vehicle with tiltable rotors. Using our informative trajectories results in models which outperform models obtained from noninformative trajectory by 13.3\% with the same amount of training data. Furthermore, we show that the model learned from informative trajectories generalizes better than the one learned from non-informative trajectories, achieving better tracking performance on different tasks.</p>},
	author = {Weixuan Zhang and Marco Tognon and Lionel Ott and Roland Siegwart and Juan Nieto}
}
@conference {125,
	title = {Direct Force and Pose NMPC with Multiple Interaction Modes for Aerial Push-and-Slide Operations},
	booktitle = {2021 IEEE Int. Conf. on Robotics and Automation},
	year = {2021},
	month = {2021/04},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Xi{\textquoteright}an, China},
	abstract = {<p>In this paper, we present a model predictive controller for a fully actuated aerial manipulator to track a hybrid force and pose trajectory at the end-effector in an aerial interaction task. A force sensor at the end-effector is used to detect contact and to directly control the interaction force. We propose an approach for automatic transition between three operation modes which reflect the state of contact constraints, including free flight and two modes for force control based on static or dynamic friction at the end-effector. For each mode, desired performance is defined with mode-specific weight matrices in the optimization problem, and through a practical implementation of contact constraints in the system dynamics formulation. Results from flight experiments, which combine force, position, and attitude tracking, show the performance of the controller in terms of accuracy and precision. The performance is further benchmarked against a hybrid force/impedance controller.</p>},
	author = {Lazar Peric and Maximilian Brunner and Karen Bodie and Marco Tognon and Roland Siegwart}
}
@article {130,
	title = {Dynamic End Effector Tracking with an Omnidirectional Parallel Aerial Manipulator},
	journal = {IEEE Robotics and Automation Letters},
	volume = {6},
	year = {2021},
	month = {2021/08},
	pages = {8165-8172},
	url = {https://ieeexplore.ieee.org/document/9507086},
	author = {Karen Bodie and Marco Tognon and Roland Siegwart}
}
