Thorsten P. Spexard

Human-Oriented Interaction With an Anthropomorphic Robot

A very important aspect in developing robots capable of human-robot interaction (HRI) is the research in natural, human-like communication, and subsequently, the development of a research platform with multiple HRI capabilities for evaluation. Besides a flexible dialog system and speech understanding, an anthropomorphic appearance has the potential to support intuitive usage and understanding of a robot, e.g., human-like facial expressions and deictic gestures can as well be produced and also understood by the robot. As a consequence of our effort in creating an anthropomorphic appearance and to come close to a human- human interaction model for a robot, we decided to use human-like sensors, i.e., two cameras and two microphones only, in analogy to human perceptual capabilities too. Despite the challenges resulting from these limits with respect to perception, a robust attention system for tracking and interacting with multiple persons simultaneously in real time is presented. The tracking approach is sufficiently generic to work on robots with varying hardware, as long as stereo audio data and images of a video camera are available. To easily implement different interaction capabilities like deictic gestures, natural adaptive dialogs, and emotion awareness on the robot, we apply a modular integration approach utilizing XML-based data exchange. The paper focuses on our efforts to bring together different interaction concepts and perception capabilities integrated on a humanoid robot to achieve comprehending human-oriented interaction.

BIRON, where are you? Enabling a robot to learn new places in a real home environment by integrating spoken dialog and visual localization

An ambitious goal in modern robotic science is to build mobile robots that are able to interact as companions in real world environments. Especially for caretaking of elderly people a system robustly working at private homes is essential, requiring a very natural and human oriented way of communication. Since home environments are usually very individual a first task for a newly acquired robot is to get familiar with its new environment. This paper gives a short overview on how we integrated a vision based localization using the advantages of a very modular architecture and extending a spoken dialog system for online labeling and interaction about different locations. We present results from the integrated system working in a real, fully furnished home environment where it was able to learn the names of different rooms. This system enables us to perform real user studies in future without the need to fall back to Wizard-of-Oz experiments. Ongoing work aims at enabling the robot to take initiative by asking for unknown locations. A future extension is the ability to generalize over features of known rooms to make predictions when encountering unknown rooms

Mixed-initiative in human augmented mapping

In scenarios that require a close collaboration and knowledge transfer between inexperienced users and robots, the “learning by interacting” paradigm goes hand in hand with appropriate representations and learning methods. In this paper we discuss a mixed initiative strategy for robotic learning by interacting with a user in a joint map acquisition process. We propose the integration of an environment representation approach into our interactive learning framework. The environment representation and mapping system supports both user driven and data driven strategies for the acquisition of spatial information, so that a mixed initiative strategy for the learning process is realised. We evaluate our system with test runs according to the scenario of a guided tour, extending the area of operation from structured laboratory environment to less predictable domestic settings.

Human-like person tracking with an anthropomorphic robot

A very important aspect in developing robots capable of human-robot interaction (HRI) is natural, human-like communication. Besides a flexible dialog system and speech understanding an anthropomorphic appearance has many advantages for intuitive usage and understanding of a robot. As a consequence of our effort in creating an anthropomorphic appearance and to come as close as possible to a human-human interaction, we decided to use human-like sensors, i.e., two cameras and two microphones only, not using a laser range finder or omnidirectional camera for tracking persons. Despite the challenge of a limited field of perception, a robust attention system for tracking and interacting with multiple persons simultaneously in real-time was created. Our approach is sufficiently generic to work on robots with varying hardware, as long as stereo audio data and images of a video camera are available. Since the architecture is designed modular with a XML based data exchange we are able to extend the robots abilities easily

Where is this? - gesture based multimodal interaction with an anthropomorphic robot

Traditional visitor guidance often suffers from the representational gap between 2D map representations and the real-world. Therefore, we propose a robotic information system that exploits its physical embodiment to present a readily interpretable interface for visitor guidance. Similar to human receptionists, it offers a familiar point of reference that can be approached by visitors and supports intuitive interaction through both speech and gesture. We focus on employing an anthropomorphic body to improve guidance functionality and interpretability of the interaction. The map, which contains knowledge about the environment, is used by robot and visitor simultaneously, with the robot translating its content into gestures. This setting affords disambiguation of information requests and thus improves robustness. It has been tested both in a laboratory demonstration setting and in our university hall, where people asked for information and thereby used the system in a natural way.

System Integration Supporting Evolutionary Development and Design

With robotic systems entering our daily life, they have to become more flexible and subsuming a multitude of abilities in one single integrated system. Subsequently an increased extensibility of the robots’ system architectures is needed. The goal is to facilitate a long-time evolution of the integrated system in-line with the scientific progress on the algorithmic level. In this paper we present an approach developed for an event-driven robot architecture, focussing on the coordination and interplay of new abilities and components. Appropriate timing, sequencing strategies, execution guaranties, and process flow synchronization are taken into account to allow appropriate arbitration and interaction between components as well as between the integrated system and the user. The presented approach features dynamic reconfiguration and global coordination based on simple production rules. These are applied first time in conjunction with flexible representations in global memory spaces and an event-driven architecture. As a result a highly adaptive robot control compared to alternative approaches is achieved, allowing system modification during runtime even within complex interactive human-robot scenarios.

Towards Tutoring an Interactive Robot

Many classical approaches developed so far for learning in a human-robot interaction setting have focussed on rather low level motor learning by imitation. Some doubts, however, have been casted on whether with this approach higher level functioning will be achieved. Higher level processes include, for example, the cognitive capability to assign meaning to actions in order to learn from the tutor. Such capabilities involve that an agent not only needs to be able to mimic the motoric movement of the action performed by the tutor. Rather, it understands the constraints, the means and the goal(s) of an action in the course of its learning process. Further support for this hypothesis comes from parent-infant instructions where it has been observed that parents are very sensitive and adaptive tutors who modify their behavior to the cognitive needs of their infant. Based on these insights, we have started our research agenda on analyzing and modeling learning in a communicative situation by analyzing parent-infant instruction scenarios with automatic methods. Results confirm the well known observation that parents modify their behavior when interacting with their infant. We assume that these modifications do not only serve to keep the infant’s attention but do indeed help the infant to understand the actual goal of an action including relevant information such as constraints and means by enabling it to structure the action into smaller, meaningful chunks. We were able to determine first objective measurements from video as well as audio streams that can serve as cues for this information in order to facilitate learning of actions.