@article {3326, title = {Decentralized Trajectory Tracking Control for Soft Robots Interacting with the Environment}, journal = {IEEE Transactions on Robotics (T-RO).}, volume = {Early Access}, year = {2018}, month = {06/2018}, abstract = {

Despite the classic nature of the problem, trajectory

tracking for soft robots, i.e. robots with compliant elements

deliberately introduced in their design, still presents several

challenges. One of these is to design controllers which can

obtain sufficiently high performance while preserving the physical

characteristics intrinsic to soft robots. Indeed, classic control

schemes using high gain feedback actions fundamentally alter the

natural compliance of soft robots effectively stiffening them, thus

de facto defeating their main design purpose. As an alternative

approach, we consider here to use a low-gain feedback, while

exploiting feedforward components. In order to cope with the

complexity and uncertainty of the dynamics, we adopt a decentralized,

iteratively learned feedforward action, combined with

a locally optimal feedback control. The relative authority of the

feedback and feedforward control actions adapts with the degree

of uncertainty of the learned component. The effectiveness of the

method is experimentally verified on several robotic structures

and working conditions, including unexpected interactions with

the environment, where preservation of softness is critical for

safety and robustness.

}, keywords = {Robotics}, author = {F. Angelini and C. Della Santina and M. Garabini and M. Bianchi and G M Gasparri and G. Grioli and M. G. Catalano and A. Bicchi} } @article {3447, title = {Incrementality and Hierarchies in the Enrollment of Multiple Synergies for Grasp Planning}, journal = {IEEE Robotics and Automation Letters}, volume = {3}, year = {2018}, month = {July}, pages = {2686-2693}, keywords = {19-DoF anthropomorphic hand, Biomechanics, common grasping tasks, compliant joint/mechanism, covariance matrices, experimental covariance matrix, Force, force distribution, grasp planning, Grasping, grippers, hand posture reconstruction, hand-object relative pose, incremental learning algorithm, Jacobian matrices, joint angle covariation patterns, learning (artificial intelligence), minimisation, multifingered hands, pose estimation, postural hand synergies, postural synergies, posture description, principal component analysis, reduced complexity representation, relative statistical weight, Robots, Solid modeling, synergy vectors, Task analysis}, doi = {10.1109/LRA.2018.2829027}, author = {G. Averta and F. Angelini and M. Bonilla and M. Bianchi and A. Bicchi} } @conference {3718, title = {On the role of postural synergies for grasp force generation and upper limb motion control}, booktitle = {International Conference on NeuroRehabilitation}, year = {2018}, url = {https://link.springer.com/chapter/10.1007/978-3-030-01845-0_69}, author = {G. Averta and F. Angelini and A. Bicchi and G. Valenza and M. Bianchi} } @article {3119, title = {Controlling Soft Robots: Balancing Feedback and Feedforward Elements}, journal = {IEEE Robotics and Automation Magazine}, volume = {24}, year = {2017}, note = {

This work is supported by European Commission grant H2020-ICT-645599 ({\textquotedblleft}SOMA{\textquotedblright}: SOft Manipulation) and European Research Council Advanced grant 291166 ({\textquotedblleft}SoftHands{\textquotedblright}).

}, month = {05/2017}, pages = {75 - 83}, abstract = {

Soft robots (SRs) represent one of the most significant recent evolutions in robotics. Designed to embody safe and natural behaviors, they rely on compliant physical structures purposefully designed to embody desirable and sometimes variable impedance characteristics. This article discusses the problem of controlling SRs. We start by observing that most of the standard methods of robotic control{\textemdash}e.g., high-gain robust control, feedback linearization, backstepping, and active impedance control{\textemdash}effectively fight against or even completely cancel the physical dynamics of the system, replacing them with a desired model. This defeats the purpose of introducing physical compliance. After all, what is the point of building soft actuators if we then make them stiff by control? An alternative to such approaches can be conceived by observing humans, who can obtain good motion accuracy and repeatability while maintaining the intrinsic softness of their bodies. In this article, we show that an anticipative model of human motor control, using a feedforward action combined with low-gain feedback, can be used to achieve human-like behavior. We present an implementation of such an idea that uses iterative learning control. Finally, we present the experimental results of the application of such learned anticipative control to a physically compliant robot. The control application achieves the desired behavior much better than a classical feedback controller used for comparison.

}, keywords = {Robotics}, issn = {1070-9932}, doi = {10.1109/MRA.2016.2636360}, url = {http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7930438}, author = {C. Della Santina and M. Bianchi and G. Grioli and F. Angelini and M. G. Catalano and M. Garabini and A. Bicchi} }