01755nas a2200169 4500008003900000245008200039210006900121260004300190520112600233100001701359700002101376700001801397700001801415700001501433700002301448856011401471 2017 d00aCerebellar-Inspired Learning Rule for Gain Adaptation of Feedback Controllers0 aCerebellarInspired Learning Rule for Gain Adaptation of Feedback a3-6 July 2017 in Valletta, MaltabIEEE3 a

The cerebellum is a crucial brain structure in enabling precise motor control in animals. Recent advances suggest that the timing of the plasticity rule of Purkinje cells, the main cells of the cerebellum, is matched to behavioral function.Simultaneously, counter-factual predictive control (CFPC), a cerebellar-based control scheme, has shown that the optimal rule for learning feed-forward action in an adaptive filter playing the role of the cerebellum must include a forward model of the system controlled. Here we show how the same learning rule obtained in CFPC, which we term as Model-enhanced least mean squares (ME-LMS), emerges in the problem of learning the gains of a feedback controller. To that end, we frame a model-reference adaptive control (MRAC) problem and derive an adaptive control scheme treating the gains of a feedback controller as if they were the weights of an adaptive linear unit. Our results demonstrate that the approach of controlling plasticity with a forward model of the subsystem controlled can provide a solution to a wide set of adaptive control problems

1 aHerreros, I.1 aArsiwalla, X. D.1 aDella Santina1 aPuigbo, J.-Y.1 aBicchi, A.1 aVerschure, P F M J uhttp://95.110.227.16/publications/cerebellar-inspired-learning-rule-gain-adaptation-feedback-controllers.html02174nas a2200229 4500008004100000022001400041245007300055210006900128260001200197300001200209490000700221520152100228653001301749100001801762700001601780700001501796700001701811700002001828700001701848700001501865856006401880 2017 eng d a1070-993200aControlling Soft Robots: Balancing Feedback and Feedforward Elements0 aControlling Soft Robots Balancing Feedback and Feedforward Eleme c05/2017 a75 - 830 v243 a

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—e.g., high-gain robust control, feedback linearization, backstepping, and active impedance control—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.

10aRobotics1 aDella Santina1 aBianchi, M.1 aGrioli, G.1 aAngelini, F.1 aCatalano, M. G.1 aGarabini, M.1 aBicchi, A. uhttp://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=793043801295nas a2200169 4500008003900000020002200039245007900061210006900140260003400209520075400243653001300997100001401010700001801024700001501042700001901057856004901076 2015 d a978-1-4799-8736-800aCoordination of unmanned marine vehicles for asymmetric threats protection0 aCoordination of unmanned marine vehicles for asymmetric threats  aMay 18-21, Genoa, ItalybIEEE3 a

A coordination protocol for systems of unmanned marine vehicles is proposed for protection against asymmetric threats. The problem is first modelled in a game theoretic framework, as a potential game. Then an extension of existing learning algorithms is proposed to address the problem of tracking the possibly moving threat. The approach is evaluated in scenarios of different geometric complexity such as open sea, bay, and harbours. Performance of the approach is evaluated in terms of a security index that will allow us to obtain a tool for team sizing. The tool provides the minimum number of marine vehicles to be used in the system, given a desired security level to be guaranteed and the maximum threat velocity.

10aRobotics1 aNardi, S.1 aDella Santina1 aMeucci, D.1 aPallottino, L. uhttp://ieeexplore.ieee.org/document/7271413/