Doreen Jirak

Istituto Italiano di Tecnologia | IIT · COgNiTive Architecture for Collaborative Technologies (CONTACT)

We present a research outline that aims at investigating group dynamics and peer pressure in the context of industrial robots. Our research plan was motivated by the fact that industrial robots became already an integral part of human-robot co-working. However, industrial robots have been sparsely integrated into research on robot credibility, grou...

As robots are expected to get more involved in people’s everyday lives, frameworks that enable intuitive user interfaces are in demand. Hand gesture recognition systems provide a natural way of communication and, thus, are an integral part of seamless human-robot interaction (HRI). Recent years have witnessed an immense evolution of computational m...

Manufacturing companies continuously integrate robots for collaboration with human workers. That challenges the design of a safe and ergonomic worker’s place to avoid collisions with the robot or other possible accidents that can severely injure a human. As many tasks in manufacturing premises entail repetitive tasks like carrying heavy loads back...

Although deep learning models are state-of-the-art models in audio classification, they fall short when applied in developmental robotic settings and human–robot interaction (HRI). The major drawback is that deep learning relies on supervised training with a large amount of data and annotations. In contrast, developmental learning strategies in hum...

Designing a humanoid robot to assist in performing cognitive multitasking is not straightforward, as the robot’s communication itself could interfere with humans’ concentration on the tasks. Therefore, we focused on the social facilitation effect caused by the presence of social robots. According to our study, a social robot that expressed its will...

Human-robot interaction (HRI) benefits greatly from advances in the machine learning field as it allows researchers to employ high-performance models for perceptual tasks like detection and recognition. Especially deep learning models, either pre-trained for feature extraction or used for classification, are now established methods to characterize...

Human-robot interaction (HRI) benefits greatly from advances in the machine learning field as it allows researchers to employ high-performance models for perceptual tasks like detection and recognition. Especially deep learning models, either pre-trained for feature extraction or used for classification, are now established methods to characterize...

The purpose of this research is to contribute to social communication between humans and robots in scenes that have been considered difficult due to the limited facial expression capabilities of robots. In order to provide more detailed facial expressions, we designed a novel wire-driven 3D eyebrow using a soft material with a bending structure. We...

A key goal in human-robot interaction (HRI) is to design scenarios between humanoid robots and humans such that the interaction is perceived as collaborative and natural, yet safe and comfortable for the human. Human skills like verbal and non-verbal communication are essential elements as humans tend to attribute social behaviors to robots. Howeve...

Whenever we are addressing a specific object or refer to a certain spatial location, we are using referential or deictic gestures usually accompanied by some verbal description. Particularly, pointing gestures are necessary to dissolve ambiguities in a scene and they are of crucial importance when verbal communication may fail due to environmental...

Recent developments of sensors that allow tracking of human movements and gestures enable rapid progress of applications in domains like medical rehabilitation or robotic control. Especially the inertial measurement unit (IMU) is an excellent device for real-time scenarios as it rapidly delivers data input. Therefore, a computational model must be...

This paper is a sequel on posture recognition using sparse autoencoders. We conduct experiments on a posture dataset and show that shallow sparse autoencoders achieve even better performance compared to a convolutional neural network, state-of-the-art model for recognition tasks. Also, our results support robust image representation from the autoen...

We present a novel gesture recognition system for the application of continuous gestures in mobile devices. We explain how meaningful gesture data can be extracted from the inertial measurement unit of a mobile phone and introduce a segmentation scheme to distinguish between different gesture classes. The continuous sequences are fed into an Echo S...

With modern computer vision techniques being successfully developed for a variety of tasks, extracting meaningful knowledge from complex scenes with multiple people still poses problems. Consequently, experiments with application-specific motion, such as gesture recognition scenarios, are often constrained to single person scenes in the literature....

Abstract—Among different gesture types, static gestures or postures deliver a broad range of communicative information like commands or emblems. Vision-based processing for posture recognition is the most intuitive yet challenging task in intelligent systems. Achievements in deep learning, specifically convolutional neural networks (CNN), replaced...

Although Deep Neural Networks reached remarkable performance on several benchmarks and even gained scientific publicity, they are not able to address the concept of cognition as a whole. In this paper, we argue that those architectures are potentially interesting for cognitive robots regarding their perceptual representation power for audio and vis...

The concept of affordances indicates "action possibilities" as characterized by object properties the environment provides to interacting organisms. Affordances relate to both perception and action and refer to sensory-motor processes emerging from goal-directed object interaction. In contrast to stable properties, affordances may vary with environ...

Emotional state recognition has become an important topic for human-robot interaction in the past years. By determining emotion expressions, robots can identify important variables of human behavior and use these to communicate in a more human-like fashion and thereby extend the interaction possibilities. Human emotions are multimodal and spontaneo...

In the last decade, training recurrent neural networks (RNN) using techniques from the area of reservoir computing (RC) became more attractive for learning sequential data due to the ease of network training. Although successfully applied in the language and speech domains, only little is known about using RC techniques for dynamic gesture recognit...

Dynamic gesture recognition is one of the most interesting and challenging areas of Human-Robot-Interaction (HRI). Problems like image segmentation, temporal and spatial feature extraction and real time recognition are the most promising issues to name in this context. This work proposes a deep neural model to recognize dynamic gestures with minima...

Gesture recognition is an important task in Human-Robot Interaction (HRI) and the research effort towards robust and high-performance recognition algorithms is increasing. In this work, we present a neural network approach for learning an arbitrary number of labeled training gestures to be recognized in real time. The representation of gestures is...

Hand pose estimation is the task of deriving a hand's articulation from sensory input, here depth images in particular. A novel approach states pose estimation as an optimization problem: a high-dimensional hypothesis space is constructed from a hand model, in which particle swarms search for the best pose hypothesis. We propose various additions t...

Face detection is an active research area comprising the fields of computer vision, machine learning and intelligent robotics. However, this area is still challenging due to many problems arising from image processing and the further steps necessary for the detection process. In this work we focus on Hopfield Neural Network (HNN) and ensemble learn...

The new concept of embodied cognition theories has been enthusiastically studied by the cognitive sciences, by as well as such disparate disciplines as philosophy, anthropology, neuroscience, and robotics. Embodiment theory provides the framework for ongoing discussions on the linkage between "low" cognitive processes as perception and "high" cogni...