@article {3621,
	title = {Learning From Humans How to Grasp: A Data-Driven Architecture for Autonomous Grasping With Anthropomorphic Soft Hands},
	journal = {IEEE Robotics and Automation Letters},
	volume = {4},
	year = {2019},
	month = {April},
	pages = {1533-1540},
	keywords = {Computer architecture, Control, Deep Learning in Robotics and Automation, Grasping, Learning for Soft Robots, Modeling, Natural Machine Motion, Neural networks, Robot sensing systems, Uncertainty, Videos},
	issn = {2377-3766},
	doi = {10.1109/LRA.2019.2896485},
	author = {C. D. Santina and V. Arapi and G. Averta and F. Damiani and G. Fiore and A Settimi and M. G. Catalano and D. Bacciu and A. Bicchi and M. Bianchi}
}
@article {3474,
	title = {A Game Theoretic Robotic Team Coordination Protocol For Intruder Herding},
	journal = {IEEE Robotics and Automation Letters},
	volume = {3},
	year = {2018},
	month = {10/2018},
	pages = {4124-4131},
	keywords = {autonomous agents, Cost function, distributed robot systems, game theory, Games, Protocols, Robot kinematics, Robot sensing systems},
	issn = {2377-3766},
	doi = {10.1109/LRA.2018.2857004},
	author = {S. Nardi and F. Mazzitelli and L. Pallottino}
}
@article {3450,
	title = {Hap-Pro: a wearable haptic device for proprioceptive feedback},
	journal = {IEEE Transactions on Biomedical Engineering},
	year = {2018},
	pages = {1-1},
	keywords = {haptic feedback, haptic interfaces, proprioception, Prosthetic hand, Robot sensing systems, Skin, upper extremity prosthesis, Visualization, Wheels},
	issn = {0018-9294},
	doi = {10.1109/TBME.2018.2836672},
	author = {M. Rossi and M. Bianchi and E. Battaglia and M. G. Catalano and A. Bicchi}
}
@article {3446,
	title = {Humanoids at Work: The WALK-MAN Robot in a Postearthquake Scenario},
	journal = {IEEE Robotics Automation Magazine},
	year = {2018},
	pages = {1-1},
	keywords = {Buildings, Earthquakes, Hardware, Legged locomotion, Robot sensing systems, Task analysis},
	issn = {1070-9932},
	doi = {10.1109/MRA.2017.2788801},
	author = {F. Negrello and A Settimi and D. Caporale and G. Lentini and M. Poggiani and D. Kanoulas and L. Muratore and Luberto, E. and G. Santaera and L. Ciarleglio and L. Ermini and L. Pallottino and D. G. Caldwell and N. Tsagarakis and A. Bicchi and M. Garabini and M. G. Catalano}
}
@conference {3796,
	title = {Optimal Active Sensing with Process and Measurement Noise},
	booktitle = {2018 IEEE International Conference on Robotics and Automation (ICRA)},
	year = {2018},
	month = {May},
	pages = {2118-2125},
	abstract = {<p>The goal of this paper is to increase the estimation performance of an Extended Kalman Filter for a nonlinear differentially flat system by planning trajectories able to maximize the amount of information gathered by onboard sensors in presence of both process and measurement noises. In a previous work, we presented an online gradient descent method for planning optimal trajectories along which the smallest eigenvalue of the Observability Gramian (OG) is maximized. As the smallest eigenvalue of the OG is inversely proportional to the maximum estimation uncertainty, its maximization reduces the maximum estimation uncertainty of any estimation algorithm employed during motion. However, the OG does not consider the process noise that, instead, in several applications is far from being negligible. For this reason, this paper proposes a novel solution able to cope with non-negligible process noise: this is achieved by minimizing the largest eigenvalue of the a posteriori covariance matrix obtained by solving the Continuous Riccati Equation as a measure of the total available information. This minimization is expected to maximize the information gathered by the outputs while, at the same time, limiting as much as possible the negative effects of the process noise. We apply our method to a unicycle robot. The comparison between the novel method and the one of our previous work (which did not consider process noise) shows significant improvements in the obtained estimation accuracy.</p>
},
	keywords = {continuous Riccati equation, covariance matrices, eigenvalues and eigenfunctions, Estimation, estimation algorithm, gradient methods, inversely proportional, Kalman Filter, Kalman filters, largest eigenvalue, maximum estimation uncertainty, measurement noise, mobile robots, noise measurement, nonlinear differentially flat system, nonlinear filters, nonnegligible process noise, observability, observability gramian, OG, onboard sensors, online gradient descent method, optimal active sensing, optimal trajectories, optimisation, path planning, posteriori covariance matrix, Riccati equations, Robot sensing systems, Trajectory, trajectory control, unicycle robot},
	doi = {10.1109/ICRA.2018.8460476},
	url = {https://ieeexplore.ieee.org/document/8460476},
	author = {M. Cognetti and P Salaris and P. Robuffo Giordano}
}
@article {3448,
	title = {Simplifying Telerobotics: Wearability and Teleimpedance Improves Human-Robot Interactions in Teleoperation},
	journal = {IEEE Robotics Automation Magazine},
	volume = {25},
	year = {2018},
	month = {March},
	pages = {77-88},
	keywords = {application fields, augmented teleoperation, Autonomous robots, communication, effective design, effective simplification, environmental constraints, feedback, Force feedback, fundamental requirement, haptic feedback devices, haptic interfaces, human workspace, human-robot interaction, human-robot interactions, human-robot interfaces, ideal scenario, integrated approach, integrated interface, integrated system, interaction forces, intuitive information exchange, Kinematics, KUKA lightweight robotic arm, lightweight design, manipulators, master, position control, reduced versions, Robot sensing systems, robotic devices, robotic hand-arm system, robotic manipulator, robotic teleoperation, simplified information exchange, slave robot, stiffness control, synergy, Task analysis, teleimpedance techniques, Telemedicine, teleoperator system, telerobotics, TI control, vision, wearability, wearable feedback, wearable hand/arm},
	issn = {1070-9932},
	doi = {10.1109/MRA.2017.2741579},
	author = {S. Fani and S. Ciotti and M. G. Catalano and G. Grioli and A. Tognetti and G. Valenza and A. Ajoudani and M. Bianchi}
}
