A discrete/rhythmic pattern generating RNN

Biological research supports the concept that advanced motion emerges from modular building blocks, which generate both rhythmical and discrete patterns. Inspired by these ideas, roboticists try to implement such building blocks using different techniques. In this paper, we show how to build such module by using a recurrent neural network (RNN) to encapsulate both discrete and rhythmical motion patterns into a single network. We evaluate the proposed system on a planar robotic manipulator. For training, we record several handwriting motions by back driving the robot manipulator. Finally, we demonstrate the ability to learn multiple motions (even discrete and rhythmic) and evaluate the pattern generation robustness in the presence of perturbations

Starting from scratch: experimenting with computer science in Flemish secondary education

In the Flemish secondary education curriculum, as in many countries and regions, computer science currently only gets an extremely limited coverage. Recently, in Flanders (and elsewhere), it has been proposed to change this, and try-outs are undertaken, both in and outside of schools. In this paper, we discuss some of those efforts, and in particular take a closer look at the preliminary results of one experiment involving different approaches to programming in grade 8. These experiments indicate that many students from secondary schools would welcome a more extensive treatment of computer science. Planning and implementing such a treatment, however, raises a number of issues, from which in this paper, we formulate a handful as calls for action for the computer science education research community

Feedback control by online learning an inverse model

A model, predictor, or error estimator is often used by a feedback controller to control a plant. Creating such a model is difficult when the plant exhibits nonlinear behavior. In this paper, a novel online learning control framework is proposed that does not require explicit knowledge about the plant. This framework uses two learning modules, one for creating an inverse model, and the other for actually controlling the plant. Except for their inputs, they are identical. The inverse model learns by the exploration performed by the not yet fully trained controller, while the actual controller is based on the currently learned model. The proposed framework allows fast online learning of an accurate controller. The controller can be applied on a broad range of tasks with different dynamic characteristics. We validate this claim by applying our control framework on several control tasks: 1) the heating tank problem (slow nonlinear dynamics); 2) flight pitch control (slow linear dynamics); and 3) the balancing problem of a double inverted pendulum (fast linear and nonlinear dynamics). The results of these experiments show that fast learning and accurate control can be achieved. Furthermore, a comparison is made with some classical control approaches, and observations concerning convergence and stability are made

The spectral radius remains a valid indicator of the echo state property for large reservoirs

In the field of Reservoir Computing, scaling the spectral radius of the weight matrix of a random recurrent neural network to below unity is a commonly used method to ensure the Echo State Property. Recently it has been shown that this condition is too weak. To overcome this problem, other more involved - sufficient conditions for the Echo State Property have been proposed. In this paper we provide a large-scale experimental verification of the Echo State Property for large recurrent neural networks with zero input and zero bias. Our main conclusion is that the spectral radius method remains a valid indicator of the Echo State Property; the probability that the Echo State Property does not hold, drops for larger networks with spectral radius below unity, which are the ones of practical interest

Developing an embodied gait on a compliant quadrupedal robot

Incorporating the body dynamics of compliant robots into their controller architectures can drastically reduce the complexity of locomotion control. An extreme version of this embodied control principle was demonstrated in highly compliant tensegrity robots, for which stable gait generation was achieved by using only optimized linear feedback from the robot's sensors to its actuators. The morphology of quadrupedal robots has previously been used for sensing and for control of a compliant spine, but never for gait generation. In this paper, we successfully apply embodied control to the compliant, quadrupedal Oncilla robot. As initial experiments indicated that mere linear feedback does not suffice, we explore the minimal requirements for robust gait generation in terms of memory and nonlinear complexity. Our results show that a memory-less feedback controller can generate a stable trot by learning the desired nonlinear relation between the input and the output signals. We believe this method can provide a very useful tool for transferring knowledge from open loop to closed loop control on compliant robots

Bringing computer science education to secondary school : a teacher first approach

The Progra-MEER professional development workshop is a one year program organized collaboratively by the computer science departments of three Flemish universities. It aims to improve the computer science knowledge of in service teachers in a physical computing context. Since Flemish schools are starting to implement STEM in their schools, the program links computer science to STEM and project based learning. This paper gives a description of the design and implementation of the program while providing an analysis of its strengths and weaknesses. We show that the program leads to the successful implementation of different physical computing projects. However, it needs to further support the practical project implementations while spending more attention on assessment and context definition. Additionally, the program has to invest more effort in creating a sustainable community of practice so knowledge and experiences can still be shared even after the program has finished

Teacher professional development through a physical computing workshop

In recent years there has been a push towards more CS and STEM education in Flanders. These two domains require a set of skills with which teachers are currently often unfamiliar. To enable teachers to acquire these skills, professional development programs should be implemented. In this paper we first present a way of identifying the properties of such a program to allow comparison with other programs. Next, we describe a professional development program in the form of a physical computing workshop

Unsupervised orientation learning using autoencoders

We present a method to learn the orientation of symmetric objects in real-world images in an unsupervised way. Our method explicitly maps in-plane relative rotations to the latent space of an autoencoder, by rotating both in the image domain and latent domain. This is achieved by adding a proposed crossing loss to a standard autoencoder training framework which enforces consistency between the image domain and latent domain rotations. This relative representation of rotation is made absolute, by using the symmetry of the observed object, resulting in an unsupervised method to learn the orientation. Furthermore, orientation is disentangled in latent space from other descriptive factors. We apply this method on two real-world datasets: aerial images of planes in the DOTA dataset and images of densely packed honeybees. We empirically show this method can learn orientation using no annotations with high accuracy compared to the same models trained with annotations