Completed and awarded PhD projects

Johannes Twiefel
Robust Bidirectional Processing for Speech-controlled Robotic Scenarios (2020)

In this thesis, we present a self-trained ASR system that performs better than Google’s cloud-based ASR on a benchmark data set. We also define a novel and simple natural language representation called Semantic Logic Predicates (SemaPreds). In our experiments, we show that we can successfully recognize SemaPreds from speech input. The approaches we developed make it possible to interpret SemaPreds, find and correct errors inside them, and evaluate their plausibility regarding a given situation. We test our novel bidirectional processing chain in a human-robot interaction scenario and show that it works robustly and performs better than a unidirectional processing pipeline. These results indicate that the novel representation and the bidirectional processing chain can be useful for other speech-controlled system scenarios.

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Muhammad Burhan Hafez
Intrinsically Motivated Actor-Critic for Robot Motor Learning (2020)

The performance of deep actor-critic reinforcement learning critically depends on the chosen exploration strategy. Random, undirected exploration is impractical in real-world robot learning and limits how quickly a robot can learn useful control policies. Another issue inevitable in complex robot learning tasks is using imperfect environment models for planning actions or generating experience, which results in a compounding of model errors and leads to poor task performance. In this thesis, we propose behaviorally and neurally plausible approaches to address these challenges associated with improving deep reinforcement learning for robot control. We show how learning progress-based intrinsic motivation can provide a directed exploration behavior and enable adaptive arbitration between model-based and model-free control, improving the sample efficiency of learning pixel-level control policies. Our intrinsic motivation encourages actions that lead to data that improves the model, which we use to generate imagined experiences, inspired by human mental simulation of motor behavior.

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Nils Meins
Diversity-driven Hopfield Neural Network Ensembles for Face Detection (2019)

In this thesis, we present a hybrid ensemble classifier approach for face detection and head-pose estimation. A key factor of ensemble classifiers is diversity. Therefore, we aim to use simple features and classifiers and focus on increasing diversity. We use the pixel sum of several rectangles, arranged in different geometrical structures as features and two classifiers which uses template matching and an additional Hopfield Neural Network to compare a feature with a learned pattern. Finally, we combine features and classifiers to a hybrid ensemble architecture to further increase diversity.

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Jorge Dávila Chacón
Biomimetic Computation and Embodied Embedded Cognition for Spatial Audition in Humanoids (2019)

Inspired by human behaviour, we propose an embodied embedded cognition approach to improve automatic speech recognition with robots under noisy conditions using binaural sound source localisation. Humanoids allow the generation of spatial cues in the entire audible range, without additional computational costs, and by simplifying existing biomimetic models for the extraction of spatial cues we are able to understand the principles that are important to increase accuracy. Our approach halves the sentence error rate in comparison to the standard approach of downmixing the input of both channels.

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Luiza Mici
Unsupervised Learning of Human-Object Interactions with Neural Network Self-Organization (2018)

The main goal of this thesis is the modeling of unsupervised learning architectures for the recognition of human-object interactions. We apply hierarchies of growing self-organizing neural networks for processing and integrating relevant cues from body motion and the action’s context. We propose and evaluate two extensions of such architecture to account for motion and action anticipation. Finally, we investigate a learning mechanism that uses the classification error for modulating neural growth.

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Francisco Javier Cruz Naranjo
Teaching Robots With Interactive Reinforcement Learning (2017)

We investigate learning approaches, more specifically interactive reinforcement learning to perform a domestic task. We use parent-like advice to explore two set-ups: agent-agent and human-agent interaction. This thesis contributes to knowledge in terms of studying the interplay of multi-modal interactive feedback and contextual affordances. Overall, we investigate which parameters influence the interactive reinforcement learning process and show that the apprenticeship of reinforcement learning agents can be sped up by means of interactive parent-like advice, multi-modal feedback, and affordances-driven environmental models.

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Doreen Jirak
Inspection of Echo State Networks for Dynamic Gestures (2017)

We investigate Echo State Networks (ESN), which implement a new training paradigm for recurrent neural networks. We first demonstrate their gesture classification performance considering two feature sets with very distinct complexity. Second, we introduce the recurrence analysis for qualitative and quantitative description of the gesture input and the system dynamics of an ESN, and show that the methodology complements classic stability measures. Finally, we address the reservoir itself and propose an algorithm for pruning connectivity in a one-shot learning scenario.

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German I. Parisi
Multimodal Learning of Actions with Deep Neural Network Self-Organization (2017)

Understanding other people's actions plays a crucial role in our everyday lives. Human beings are able to reliably discriminate a series of socially relevant cues from body motion, with this ability being supported by highly skilled visual perception and other modalities. The main goal of this thesis is the modeling of artificial learning architectures for action perception with focus on the development of multimodal action representations. As a modeling foundation to address our research question, we focus on hierarchies of self-organizing neural networks motivated by experience-driven cortical organization.

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Pablo Vinicius Alves de Barros
Modeling Affection Mechanisms using Deep and Self-Organizing Neural Networks (2016)

One of the most important aspects of affective computing is how to make computational systems use emotion concepts in different situations. Although several types of research were done, we are still far away from having a system which can recognize and learn emotion concepts in a satisfactory way. In this thesis, we propose computational models which introduce a unified solution for emotional attention, recognition, and learning. These models are competitive in each of these tasks, and also provide an overview of a learning mechanism which adapts its knowledge according to a given situation.

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Stefan Heinrich
Natural Language Acquisition in Recurrent Neural Architectures (2016)

Our understandings of the behavioural and mechanistic characteristics for natural language are still in its infancy and we need to bridge the gap between the insights from linguistics, neuroscience, and behavioural psychology. To contribute an understanding of the appropriate characteristics in a brain-inspired neural architecture that favour language acquisition, recurrent neural models have been developed for embodied and multi-modal language processing, embedded in a developmental robotics framework. In this dissertation, the main contributions from the study of these models are reported.

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Nicolás Navarro-Guerrero
Neurocomputational Mechanisms for Adaptive Self-Preservative Robot Behaviour (2016)

We believe that a deeper understanding of innate and learned defensive mechanisms could also be helpful in developing future robot generations, making them more adaptable and robust. Therefore, in this thesis, we study and develop three neuro-computational self-preservative mechanisms in the context of humanoid service robots to demonstrate the potential and feasibility of including bio-inspired adaptive self-preservative mechanisms as part of real-world robotic systems. This thesis does not attempt to provide a comprehensive model of animal behaviour, but rather tries to draw attention to the need for it by presenting the potential of neglected aspects of animal behaviour such as self-preservative behaviour.

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Wenjie Yan
Indoor Vision-based Robot Navigation: a Neural Probabilistic Approach (2016)

We present a neural probabilistic robot localization and navigation that provides a mere concept while enabling far-reaching functionality. Through emulating several basic functionalities of the brain the system is able to achieve complex tasks such as robust target tracking, environment learning through observation, and flexible robot navigation in a home-like environment. The concept of our work is implemented and evaluated using a robot platform in a home-like environment, and the results show that our neural system helps a robot to realize different functions successfully.

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Johannes Bauer
One Computer Scientist's (Deep) Superior Colliculus. Modeling, understanding, and learning from a multisensory midbrain structure (2015)

The superior colliculus is a mid-brain region which integrates sensory input to localize multisensory stimuli. This thesis aims to close the gap between models of SC physiology and system-level behavior, and development thereof. A new model of the SC based on self-organized statistical learning is proposed and it is shown to replicate a variety of important biological phenomena. It is then applied to a problem in robotics: binaural sound-source localization. We show that our algorithm can learn to perform state-of-the-art sound-source localization.

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Junpei Zhong
Artificial Neural Models for Feedback Pathways for Sensorimotor Integrations (2014)

The brain comprises hierarchical modules on various physiological levels. Neural feedback signals modulate the neural activities via inhibitory or excitatory connections within/between these levels. They have predictive and filtering functions on the neuronal population coding of the bottom-up sensory-driven signals in the perception-action system. In this thesis, we propose that the predictive role of the feedback pathways at most levels of action and perception can be modelled by the recurrent connections in different artificial cognitive platforms. This will be examined by three recurrent neural network models. Furthermore, the three models and experiments with them show that the recurrent neural networks are able to model feedback pathways and to exhibit the feedback-related sensorimotor predictive functions.

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Jens Kleesiek
Action-driven perception – neural architectures based on sensorimotor principles (2012)

The active nature of perception and the intimate relation of action and cognition has been emphasized in philosophy and cognitive science for a long time. However, most of the current (computational) approaches do not consider the fundamental role of action for perception. Inspired by theories rooted in the research field of embodied cognition the scope of this thesis has been to design artificial neural architectures for the learning of sensorimotor laws. Guided by the core concept that an agent actually needs to act to perceive, a series of computational studies, including simulations and real-world robot experiments, have been conducted. It is shown that considering a set of common sensorimotor design principles results in goal-directed behavior of artificial agents. In this talk experiments as well as the underlying sensorimotor design principles will be presented and discussed.