Anja Austermann

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In this paper, we compare users' interaction with the humanoid robot ASIMO and the dog-shaped robot AIBO. We conducted a user study in which the participants had to teach object names and simple commands and give feedback to either AIBO or ASIMO. We did not find significant differences in the users' evaluation of both robots and in the way commands were(More)
Our study compares users’ interaction with a humanoid robot and a dog-shaped pet-robot. We conducted a user study in which the participants had to teach object names as well as simple commands to either the humanoid or the pet-robot and give feedback to the robot for correct and incorrect performance. While we found, that the way of uttering commands rather(More)
This paper describes the realization of a natural speech dialogue for the robot head MEXI with focus on its emotion recognition. Specific for MEXI is that it can recognize emotions from natural speech and also produce natural speech output with emotional prosody. For recognizing emotions from the prosody of natural speech we use a fuzzy rule based approach.(More)
This paper describes the emotion recognition from natural speech as realized for the robot head MEXI. We use a fuzzy logic approach for analysis of prosody in natural speech. Since MEXI often communicates with well known persons but also with unknown humans, for instance at exhibitions, we realized a speaker dependent mode as well as a speaker independent(More)
We are proposing an approach to enable a robot to learn the speech, gesture and touch patterns, that its user employs for giving positive and negative reward. The learning procedure uses a combination of Hidden Markov Models and a mathematical model of classical conditioning. To facilitate learning, the robot and the user go through a training task where(More)
This paper describes an experimental study in which we analyze how users give multimodal positive and negative feedback by speech, gesture and touch when teaching easy game-tasks to a pet robot. The tasks are designed to allow the robot to freely explore and provoke human reward behavior. By choosing game-based tasks, we ensure that the training can be(More)
Enabling a robot to understand natural commands for HumanRobot-Interaction is a challenge that needs to be solved to enable novice users to interact with robots smoothly and intuitively. We propose a method to enable a robot to learn how its user utters commands in order to adapt to individual differences in speech usage. The learning method combines a(More)
This paper proposes a novel method of learning a users preferred reward modalities for human-robot interaction through solving a cooperative training task. A learning algorithm based on a combination of adaptable pre-trained Hidden Markov Models and a computational model of classical conditioning is outlined. In a training task, where the desired outcome is(More)
In this paper, we present a human-robot teaching framework that uses “virtual” games as a means for adapting a robot to its user through natural interaction in a controlled environment. We present an experimental study in which participants instruct an AIBO pet robot while playing different games together on a computer generated playfield. By playing the(More)