Britta Wrede

Integration of Action and Language Knowledge: A Roadmap for Developmental Robotics

This position paper proposes that the study of embodied cognitive agents, such as humanoid robots, can advance our understanding of the cognitive development of complex sensorimotor, linguistic, and social learning skills. This in turn will benefit the design of cognitive robots capable of learning to handle and manipulate objects and tools autonomously, to cooperate and communicate with other robots and humans, and to adapt their abilities to changing internal, environmental, and social conditions. Four key areas of research challenges are discussed, specifically for the issues related to the understanding of: 1) how agents learn and represent compositional actions; 2) how agents learn and represent compositional lexica; 3) the dynamics of social interaction and learning; and 4) how compositional action and language representations are integrated to bootstrap the cognitive system. The review of specific issues and progress in these areas is then translated into a practical roadmap based on a series of milestones. These milestones provide a possible set of cognitive robotics goals and test scenarios, thus acting as a research roadmap for future work on cognitive developmental robotics.

A saliency map based on sampling an image into random rectangular regions of interest

In this article we propose a novel approach to compute an image saliency map based on computing local saliencies over random rectangular regions of interest. Unlike many of the existing methods, the proposed approach does not require any training bases, operates on the image at the original scale and has only a single parameter which requires tuning. It has been tested on the two distinct tasks of salient region detection (using MSRA dataset) and eye gaze prediction (using York University and MIT datasets). The proposed method achieves state-of-the-art performance on the eye gaze prediction task as compared with nine other state-of-the-art methods.

How can multimodal cues from child-directed interaction reduce learning complexity in robots?

Robots have to deal with an enormous amount of sensory stimuli. One solution in making sense of them is to enable a robot system to actively search for cues that help structuring the information. Studies with infants reveal that parents support the learning-process by modifying their interaction style, dependent on their childs developmental age. In our study, in which parents demonstrated everyday actions to their preverbal children (8–11 months old), our aim was to identify objective parameters for multimodal action modification. Our results reveal two action parameters being modified in adult–child interaction: roundness and pace. Furthermore, we found that language has the power to help children structuring actions sequences by synchrony and emphasis. These insights are discussed with respect to the built-in attention architecture of a socially interactive robot, which enables it to understand demonstrated actions. Our algorithmic approach towards automatically detecting the task structure in child-designed input demonstrates the potential impact of insights from developmental learning on robotics. The presented findings pave the way to automatically detect when to imitate in a demonstration task.

The bielefeld anthropomorphic robot head “Flobi”

A robots head is important both for directional sensors and, in human-directed robotics, as the single most visible interaction interface. However, designing a robots head faces contradicting requirements when integrating powerful sensing with social expression. Furher, reactions of the general public show that current head designs often cause negative user reactions and distract from the functional capabilities.

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

Understanding Social Robots

Research on social robots is mainly comprised of research into algorithmic problems in order to expand a robot´s capabilities to improve communication with human beings. Also, a large body of research concentrates on the appearance, i.e. aesthetic form of social robots. However, only little reference to their definition is made. In this paper we argue that form, function, and context have to be taken systematically into account in order to develop a model to help us understand social robots. Therefore, we address the questions: What is a social robot, what are the interdisciplinary research aspects of social robotics, and how are these different aspects interlinked? In order to present a comprehensive and concise overview of the various aspects we present a framework for a definition towards social robots.

The curious robot - Structuring interactive robot learning

If robots are to succeed in novel tasks, they must be able to learn from humans. To improve such human-robot interaction, a system is presented that provides dialog structure and engages the human in an exploratory teaching scenario. Thereby, we specifically target untrained users, who are supported by mixed-initiative interaction using verbal and non-verbal modalities. We present the principles of dialog structuring based on an object learning and manipulation scenario. System development is following an interactive evaluation approach and we will present both an extensible, event-based interaction architecture to realize mixed-initiative and evaluation results based on a video-study of the system. We show that users benefit from the provided dialog structure to result in predictable and successful human-robot interaction.

Social facilitation with social robots

Regarding the future usage of social robots in workplace scenarios, we addressed the question of potential mere robotic presence effects on human performance. Applying the experimental social facilitation paradigm in social robotics, we compared task performance of 106 participants on easy and complex cognitive and motoric tasks across three presence groups (alone vs. human present vs. robot present). Results revealed significant evidence for the predicted social facilitation effects for both human and robotic presence compared to an alone condition. Implications of these findings are discussed with regard to the consideration of the interaction of robotic presence and task difficulty in modeling robotic assistance systems.

Understanding social robots: A user study on anthropomorphism

Anthropomorphism is one of the keys to understand the expectations people have about social robots. In this paper we address the question of how a robotpsilas actions are perceived and represented in a human subject interacting with the robot and how this perception is influenced only by the appearance of the robot. We present results of an interaction-study in which participants had to play a version of the classical Prisonerspsila Dilemma Game (PDG) against four opponents: a human partner (HP), an anthropomorphic robot (AR), a functional robot (FR), and a computer (CP). As the responses of each game partner were randomized unknowingly to the participants, the attribution of intention or will to an opponent (i.e. HP, AR, FR or CP) was based purely on differences in the perception of shape and embodiment. We hypothesize that the degree of human-likeness of the game partner will modulate what the people attribute to the opponents - the more human like the robot looks the more people attribute human-like qualities to the robot.

A short review of symbol grounding in robotic and intelligent systems

This paper gives an overview of the research papers published in Symbol Grounding in the period from the beginning of the 21st century up 2012. The focus is in the use of symbol grounding for robotics and intelligent system. The review covers a number of subtopics, that include, physical symbol grounding, social symbol grounding, symbol grounding for vision systems, anchoring in robotic systems, and learning symbol grounding in software systems and robotics. This review is published in conjunction with a special issue on Symbol Grounding in the Künstliche Intelligenz Journal.