Reliable robotic handovers through tactile sensing

Joint manipulation and object exchange are common in many everyday scenarios. Although they are trivial tasks for humans, they are still very challenging for robots. Existing approaches for robot-to-human object handover assume that there is no fault during the transfer. However, unintentional perturbation forces can be occasionally applied to the object, resulting in the robot and the object being damaged, for example by being dropped. In this paper we present a novel approach to handover objects in a reliable manner while ensuring the safety of the robot and the object. Relying on tactile sensing, the system uses an effort controller to adapt the grasp forces in the presence of perturbations. Moreover, the proposed approach identifies a perturbation being applied on the object. When a perturbation event is detected, the algorithm classifies the direction of the pulling forces to decide whether to release it or not. The reliable handover system was implemented using a Shadow Robot hand equipped with BioTAC tactile sensors. Our results show that the system correctly adapts to the forces applied on the object to maintain the grasp and only releases the object if the human receiver pulls in the right direction

Multimodal material identification through recursive tactile sensing

Tactile sensing has recently been used in robotics for object identification, grasping, and material identification. Although human tactile sensing is multimodal, existing material recognition approaches use vibration information only. Moreover, material identification through tactile sensing can be solved as an continuous process, yet state of the art approaches use a batch approach where readings are taken for at least one second. This work proposes a recursive multimodal (vibration and thermal) tactile material identification approach. Using the frequency response of the vibration induced by the material and a set of thermal features, we show that it is possible to accurately identify materials in less than half a second. We conducted an exhaustive comparison of our approach with commonly used vibration descriptors and machine learning algorithms for material identification such as k-Nearest Neighbour, Artificial Neural Network and Support Vector Machines. Experimental results show that our approach identifies materials faster than existing techniques and increase the classification accuracy when multiple sensor modalities are used

Quasiperiodic Patterns in Boundary-Modulated Excitable Waves

We investigate the impact of the domain shape on wave propagation in excitable media. Channelled domains with sinusoidal boundaries are considered. Trains of fronts generated periodically at an extreme of the channel are found to adopt a quasiperiodic spatial configuration stroboscopically frozen in time. The phenomenon is studied in a model for the photo-sensitive Belousov-Zabotinsky reaction, but we give a theoretical derivation of the spatial return maps prescribing the height and position of the successive fronts that is valid for arbitrary excitable reaction-diffusion systems.Comment: 4 pages (figures included

Why fly blind? Event-based visual guidance for ornithopter robot flight

Under licence Creative Commons - Green Open Access (IEEE).The development of perception and control methods that allow bird-scale flapping-wing robots (a.k.a. ornithopters) to perform autonomously is an under-researched area. This paper presents a fully onboard event-based method for ornithopter robot visual guidance. The method uses event cameras to exploit their fast response and robustness against motion blur in order to feed the ornithopter control loop at high rates (100 Hz). The proposed scheme visually guides the robot using line features extracted in the event image plane and controls the flight by actuating over the horizontal and vertical tail deflections. It has been validated on board a real ornithopter robot with real-time computation in low-cost hardware. The experimental evaluation includes sets of experiments with different maneuvers indoors and outdoors.Consejo Europeo de Investigación (ERC) 78824

Complex cooperative networks from evolutionary preferential attachment

In spite of its relevance to the origin of complex networks, the interplay between form and function and its role during network formation remains largely unexplored. While recent studies introduce dynamics by considering rewiring processes of a pre-existent network, we study network growth and formation by proposing an evolutionary preferential attachment model, its main feature being that the capacity of a node to attract new links depends on a dynamical variable governed in turn by the node interactions. As a specific example, we focus on the problem of the emergence of cooperation by analyzing the formation of a social network with interactions given by the Prisoner's Dilemma. The resulting networks show many features of real systems, such as scale-free degree distributions, cooperative behavior and hierarchical clustering. Interestingly, results such as the cooperators being located mostly on nodes of intermediate degree are very different from the observations of cooperative behavior on static networks. The evolutionary preferential attachment mechanism points to an evolutionary origin of scale-free networks and may help understand similar feedback problems in the dynamics of complex networks by appropriately choosing the game describing the interaction of nodes.Comment: 6 pages and 4 figures, APS format. Submitted for publicatio

Mesoscopic structure conditions the emergence of cooperation on social networks

We study the evolutionary Prisoner's Dilemma on two social networks obtained from actual relational data. We find very different cooperation levels on each of them that can not be easily understood in terms of global statistical properties of both networks. We claim that the result can be understood at the mesoscopic scale, by studying the community structure of the networks. We explain the dependence of the cooperation level on the temptation parameter in terms of the internal structure of the communities and their interconnections. We then test our results on community-structured, specifically designed artificial networks, finding perfect agreement with the observations in the real networks. Our results support the conclusion that studies of evolutionary games on model networks and their interpretation in terms of global properties may not be sufficient to study specific, real social systems. In addition, the community perspective may be helpful to interpret the origin and behavior of existing networks as well as to design structures that show resilient cooperative behavior.Comment: Largely improved version, includes an artificial network model that fully confirms the explanation of the results in terms of inter- and intra-community structur

Synchronous bursts on scale-free neuronal networks with attractive and repulsive coupling

This paper investigates the dependence of synchronization transitions of bursting oscillations on the information transmission delay over scale-free neuronal networks with attractive and repulsive coupling. It is shown that for both types of coupling, the delay always plays a subtle role in either promoting or impairing synchronization. In particular, depending on the inherent oscillation period of individual neurons, regions of irregular and regular propagating excitatory fronts appear intermittently as the delay increases. These delay-induced synchronization transitions are manifested as well-expressed minima in the measure for spatiotemporal synchrony. For attractive coupling, the minima appear at every integer multiple of the average oscillation period, while for the repulsive coupling, they appear at every odd multiple of the half of the average oscillation period. The obtained results are robust to the variations of the dynamics of individual neurons, the system size, and the neuronal firing type. Hence, they can be used to characterize attractively or repulsively coupled scale-free neuronal networks with delays.Comment: 15 pages, 9 figures; accepted for publication in PLoS ONE [related work available at http://arxiv.org/abs/0907.4961 and http://www.matjazperc.com/

The Kuramoto model in complex networks

181 pages, 48 figures. In Press, Accepted Manuscript, Physics Reports 2015 Acknowledgments We are indebted with B. Sonnenschein, E. R. dos Santos, P. Schultz, C. Grabow, M. Ha and C. Choi for insightful and helpful discussions. T.P. acknowledges FAPESP (No. 2012/22160-7 and No. 2015/02486-3) and IRTG 1740. P.J. thanks founding from the China Scholarship Council (CSC). F.A.R. acknowledges CNPq (Grant No. 305940/2010-4) and FAPESP (Grants No. 2011/50761-2 and No. 2013/26416-9) for financial support. J.K. would like to acknowledge IRTG 1740 (DFG and FAPESP).Peer reviewedPreprin

ASAP: adaptive transmission scheme for online processing of event-based algorithms

Online event-based perception techniques on board robots navigating in complex, unstructured, and dynamic environments can suffer unpredictable changes in the incoming event rates and their processing times, which can cause computational overflow or loss of responsiveness. This paper presents ASAP: a novel event handling framework that dynamically adapts the transmission of events to the processing algorithm, keeping the system responsiveness and preventing overflows. ASAP is composed of two adaptive mechanisms. The first one prevents event processing overflows by discarding an adaptive percentage of the incoming events. The second mechanism dynamically adapts the size of the event packages to reduce the delay between event generation and processing. ASAP has guaranteed convergence and is flexible to the processing algorithm. It has been validated on board a quadrotor and an ornithopter robot in challenging conditions