Gordon Pipa

Farbod N. Nezami, Maximilian A. Wächter, Nora Maleki et al.

With the further development of highly automated vehicles, drivers will engage in non-related tasks while being driven. Still, drivers have to take over control when requested by the car. Here the question arises, how potentially distracted drivers get back into the control-loop quickly and safely when the car requests a takeover. To investigate effective human-machine interactions in mobile, versatile, and cost-efficient setup is needed. We developed a virtual reality toolkit for the Unity 3D game engine containing all necessary code and assets to enable fast adaptations to various human-machine interaction experiments, including close monitoring of the subject. The presented project contains all needed functionalities for realistic traffic behavior, cars, and pedestrians, as well as a large, open-source, scriptable, and modular VR environment. It covers roughly 25 square km, a package of 125 animated pedestrians and numerous vehicles, including motorbikes, trucks, and cars. It also contains all needed nature assets to make it both highly dynamic and realistic. The presented repository contains a C++ library made for LoopAR that enables force feedback for gaming steering wheels as a fully supported component. It also includes All necessary scripts for eye-tracking in the used devices. All main functions are integrated into the graphical user interface of the Unity Editor or are available as prefab variants to ease the use of the embedded functionalities. The primary purpose of this project is to serve as open access, cost-efficient toolkit that enables interested researchers to conduct realistic virtual reality research studies without costly and immobile simulators.

Viviane Clay, Peter König, Gordon Pipa et al.

Most artificial neural networks used for object detection and recognition are trained in a fully supervised setup. This is not only very resource consuming as it requires large data sets of labeled examples but also very different from how humans learn. We introduce a setup in which an artificial agent first learns in a simulated world through self-supervised exploration. Following this, the representations learned through interaction with the world can be used to associate semantic concepts such as different types of doors. To do this, we use a method we call fast concept mapping which uses correlated firing patterns of neurons to define and detect semantic concepts. This association works instantaneous with very few labeled examples, similar to what we observe in humans in a phenomenon called fast mapping. Strikingly, this method already identifies objects with as little as one labeled example which highlights the quality of the encoding learned self-supervised through embodiment using curiosity-driven exploration. It therefor presents a feasible strategy for learning concepts without much supervision and shows that through pure interaction with the world meaningful representations of an environment can be learned.

Leon René Sütfeld, Flemming Brieger, Holger Finger et al.

The most widely used activation functions in current deep feed-forward neural networks are rectified linear units (ReLU), and many alternatives have been successfully applied, as well. However, none of the alternatives have managed to consistently outperform the rest and there is no unified theory connecting properties of the task and network with properties of activation functions for most efficient training. A possible solution is to have the network learn its preferred activation functions. In this work, we introduce Adaptive Blending Units (ABUs), a trainable linear combination of a set of activation functions. Since ABUs learn the shape, as well as the overall scaling of the activation function, we also analyze the effects of adaptive scaling in common activation functions. We experimentally demonstrate advantages of both adaptive scaling and ABUs over common activation functions across a set of systematically varied network specifications. We further show that adaptive scaling works by mitigating covariate shifts during training, and that the observed advantages in performance of ABUs likewise rely largely on the activation function's ability to adapt over the course of training.