@article {3642, title = {Relaying the High-Frequency Contents of TactileFeedback to Robotic Prosthesis Users: Design,Filtering, Implementation, and Validation}, journal = {IEEE Robotics and Automation Letters}, volume = {Volume: 4 , Issue: 2 , April 2019}, year = {2019}, month = {01/2019}, author = {S. Fani and K. Di Blasio and M. Bianchi and M. G. Catalano and G. Grioli and A. Bicchi} } @conference {3121, title = {The Rice Haptic Rocker: skin stretch haptic feedback with the Pisa/IIT SoftHand }, booktitle = {IEEE World Haptics Conference}, year = {2017}, note = {

The authors gratefully acknowledge Matteo Rossi for his valuable advice and Mikaela Juzswik for her unique contribution in the physical realization of some of the equipment used in the experiments. This work was partially supported by the European Community funded project WEARHAP (contract 601165), by the European Commission project (Horizon 2020 research program) SOFTPRO (no. 688857), by the ERC Advanced Grant no. 291166 SoftHands and by the NSF grant IIS-1065497.

}, month = {06/2017}, publisher = {IEEE}, organization = {IEEE}, address = {F{\"u}rstenfeldbruck (Munich), Germany, June 6-9, 2017}, abstract = {

Myoelectric prostheses have seen increased application in clinical practice and research, due to their potential for good functionality and versatility. Yet, myoelectric prostheses still suffer from a lack of intuitive control and haptic feedback, which can frustrate users and lead to abandonment. To address this problem, we propose to convey proprioceptive information for a prosthetic hand with skin stretch using the Rice Haptic Rocker. This device was integrated with the myo-controlled version of Pisa/IIT SoftHand and a size discrimination test with 18 able bodied subjects was performed to evaluate the effectiveness of the proposed approach. Results show that the Rice Haptic Rocker can be successfully used to convey proprioceptive information. A Likert survey was also presented to the experiment participants, who evaluated the integrated setup as easy to use and effective in conveying proprioception.

}, keywords = {Haptics}, author = {E. Battaglia and J. P. Clark and M. Bianchi and M. G. Catalano and A. Bicchi and M. K. O{\textquoteright}Malley} } @conference {2889, title = {Robust Optimization of System Compliance for Physical Interaction in Uncertain Scenarios}, booktitle = {IEEE International Conference on Humanoid Robots (HUMANOIDS2016)}, year = {2016}, note = {
This work was supported by the European Commission projects (FP7 framework) Walk-Man and the European Commission Grant no. H2020- ICT-645599 {\textquotedblleft}SOMA{\textquotedblright}: SOft MAnipulation
}, publisher = {IEEE}, organization = {IEEE}, address = {Cancun, Mexico, 15-17 Nov. 2016 }, abstract = {

Compliance in robot design and control is often introduced to improve the robot performance in tasks where interaction with environment or human is required. However a rigorous method to choose the correct level of compliance is still not available. In this work we use robust optimization as a tool to select the optimal compliance value in a robotenvironment interaction scenario under uncertainties. We propose an approach that can be profitably applied on a variety of tasks, e.g.manipulation tasks or locomotion tasks. The aim is to minimize the forces of interaction considering model constraints and uncertainties. Numerical results show that: i) in case of perfect knowledge of the environment stiff robots behave better in terms of force minimization, ii) in case of uncertainties the optimal stiffness of the robot is lower than the previous case and optimal solutions provide a faster task accomplishment, iii) the optimal stiffness decreases as a function of the uncertainty measure. Experiments are carried out in a realistic set-up in case of bi-manual object handover.

}, keywords = {Robotics}, isbn = {978-1-5090-4718-5 }, doi = { 10.1109/HUMANOIDS.2016.7803381}, url = {http://ieeexplore.ieee.org/document/7803381/}, author = {Gasparri, G. M. and F. Fabiani and M. Garabini and L. Pallottino and M. G. Catalano and G. Grioli and R. Persichini and A. Bicchi} } @conference {ACBBGSFB-IROS09, title = {A Rough-Terrain, Casting Robot for the ESA Lunar Robotics Challenge}, booktitle = {Proc. IEEE/RSJ International Conference on Intelligent RObots and Systems}, year = {2009}, month = {October, 11 - 15}, pages = {3336-3342}, address = {St. Louis MO USA}, abstract = {

This paper describes the design and implementation of DAVID, a lunar vehicle developed for the European Space Agency (ESA) Lunar Robotics Challenge, presenting severe terrain negotiation and sample acquisition challenges. We discuss in some detail two of the main innovative aspects of our entry to the challenge, i.e. the locomotion system and the sample acquisition system. Motivated by the challenge specifications, a range of different locomotion systems were considered, among which we chose a simple, rugged and effective wheeled system. We provide an account of the choice of five different types of wheels, which were designed, analyzed and experimentally tested in conditions similar to the challenge. The system eventually turned out to be very effective in negotiating 89\% slopes of volcanic terrain on the challenge site, Mount Teide in Tenerife. To reduce the distance to be traveled on the difficult terrain and avoid risks in reaching the lowest parts of a crater, the vehicle was endowed with an innovative sample acquisition system, i.e. a casting manipulator. Casting manipulation is a technique in which the end-effector is thrown, the sample material is acquired, and the end-effector is retrieved using a light tether that acts as a

}, keywords = {Robotics}, author = {S. Alicino and M. G. Catalano and F. Bonomo and F. A. W. Belo and G. Grioli and R. Schiavi and A. Fagiolini and A. Bicchi} }