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Embodiment has led to a revolution in robotics by not thinking of the robot body and its controller as two separate units, but taking into account the interaction of the body with its environment. By investigating the effect of the body on the overall control computation, it has been suggested that the body is effectively performing computations, leading to the term morphological computation. Recent work has linked this to the field of reservoir computing, allowing one to endow morphologies with a theory of universal computation. In this work, we study a family of highly dynamic body structures, called tensegrity structures, controlled by one of the simplest kinds of "brains." These structures can be used to model biomechanical systems at different scales. By analyzing this extreme instantiation of compliant structures, we demonstrate the existence of a spectrum of choices of how to implement control in the body-brain composite. We show that tensegrity structures can maintain complex gaits with linear feedback control and that external feedback can intrinsically be integrated in the control loop. The various linear learning rules we consider differ in biological plausibility, and no specific assumptions are made on how to implement the feedback in a physical system.

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The simulation of spiking neural networks (SNNs) is known to be a very time-consuming task. This limits the size of SNN that can be simulated in reasonable time or forces users to overly limit the complexity of the neuron models. This is one of the driving forces behind much of the recent research on event-driven simulation strategies. Although event-driven simulation allows precise and efficient simulation of certain spiking neuron models, it is not straightforward to generalize the technique to more complex neuron models, mostly because the firing time of these neuron models is computationally expensive to evaluate. Most solutions proposed in literature concentrate on algorithms that can solve this problem efficiently. However, these solutions do not scale well when more state variables are involved in the neuron model, which is, for example, the case when multiple synaptic time constants for each neuron are used. In this letter, we show that an exact prediction of the firing time is not required in order to guarantee exact simulation results. Several techniques are presented that try to do the least possible amount of work to predict the firing times. We propose an elegant algorithm for the simulation of leaky integrate-and-fire (LIF) neurons with an arbitrary number of (unconstrained) synaptic time constants, which is able to combine these algorithmic techniques efficiently, resulting in very high simulation speed. Moreover, our algorithm is highly independent of the complexity (i.e., number of synaptic time constants) of the underlying neuron model.

We present Oncilla robot, a novel mobile, quadruped legged locomotion machine. This large-cat sized, 5.1 kg robot is one of a kind of a recent, bioinspired legged robot class designed with the capability of model-free locomotion control. Animal legged locomotion in rough terrain is clearly shaped by sensor feedback systems. Results with Oncilla robot show that agile and versatile locomotion is possible without sensory signals to some extend, and tracking becomes robust when feedback control is added (Ajallooeian, 2015b). By incorporating mechanical and control blueprints inspired from animals, and by observing the resulting robot locomotion characteristics, we aim to understand the contribution of individual components. Legged robots have a wide mechanical and control design parameter space, and a unique potential as research tools to investigate principles of biomechanics and legged locomotion control. But the hardware and controller design can be a steep initial hurdle for academic research. To facilitate the easy start and development of legged robots, Oncilla-robot's blueprints are available through open-source. The robot's locomotion capabilities are shown in several scenarios. Specifically, its spring-loaded pantographic leg design compensates for overdetermined body and leg postures, i.e. during turning maneuvers, locomotion outdoors, or while going up and down slopes. The robot's active degree of freedom allow tight and swift direction changes, and turns on the spot. Presented hardware experiments are conducted in an open-loop manner, with little control and computational 1 arXiv:1803.06259v2 [cs.RO] 16 Jun 2018 Spröwitz et al. Oncilla robot effort. For more versatile locomotion control, Oncilla-robot can sense leg joint rotations, and legtrunk forces. Additional sensors can be included for feedback control with an open communication protocol interface. The robot's customized actuators are designed for robust actuation, and efficient locomotion. It trots with a cost of transport of 3.2 J/(Nm), at a speed of 0.63 m s −1 (Froude number 0.25). The robot trots inclined slopes up to 10 • , at 0.25 m s −1 . The multi-body Webots model of Oncilla robot, and Oncilla robot's extensive software architecture enables users to design and test scenarios in simulation. Controllers can directly be transferred to the real robot. Oncilla robot's blueprints are open-source published (hardware GLP v3, software LGPL v3).

Hardware implementations of Spiking Neural Networks are numerous because they are well suited for implementation in digital and analog hardware, and outperform classic neural networks. This work presents an application driven digital hardware exploration where we implement realtime, isolated digit speech recognition using a Liquid State Machine (a recurrent neural network of spiking neurons where only the output layer is trained). First we test two existing hardware architectures, but they appear to be too fast and thus area consuming for this application. Then we present a scalable, serialised architecture that allows a very compact implementation of spiking neural networks that is still fast enough for real-time processing. This work shows that there is actually a large hardware design space of Spiking Neural Network hardware that can be explored. Existing architectures only spanned part of it.

Smart devices are omnipresent today and the design of these embedded systems requires a multidisciplinary approach. It is important that students in electrical engineering and computer science learn these different aspects of embedded systems design. Our course on Complex Systems Design Methodology presents an overview of embedded systems design with a strong focus on the main concepts, preparing the students for more detailed follow-up courses on specific topics. Imparting the theoretical concepts to the students is not sufficient, however. Hands-on sessions are indispensable for the students to acquire the necessary skills. In this article we present our approach for these hands-on sessions, which is to pose relatively small problems in separate sessions, each focusing on a single design aspect. Five years after the introduction of this new course at Ghent University, we can conclude that students not only like this course, but that their design skills have also improved by our new, aspectfocused, approach

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