Organizational Unit:

Mobile Robot Laboratory

Permanent Link

https://hdl.handle.net/1853/70779

Parent Organization

Organizational Unit

College of Computing

ArchiveSpace Name Record

https://finding-aids.library.gatech.edu/agents/corporate_entities/1110

Full item page

Publication Search Results

Now showing 1 - 10 of 76

Accountable Autonomous Agents: The next level

(Georgia Institute of Technology, 2009) Arkin, Ronald C.
Robot Deception: Recognizing when a Robot Should Deceive

(Georgia Institute of Technology, 2009) Wagner, Alan R. ; Arkin, Ronald C.

This article explores the possibility of developing robot control software capable of discerning when and if a robot should deceive. Exploration of this problem is critical for developing robots with deception capabilities and may lend valuable insight into the phenomena of deception itself. In this paper we explore deception from an interdependence/game theoretic perspective. Further, we develop and experimentally investigate an algorithm capable of indicating whether or not a particular social situation warrants deception on the part of the robot. Our qualitative and quantitative results provide evidence that, indeed, our algorithm recognizes situations which justify deception and that a robot capable of discerning these situations is better suited to act than one that does not.
Creating and Using Matrix Representations of Social Interaction

(Georgia Institute of Technology, 2009) Wagner, Alan R.

This paper explores the use of an outcome matrix as a computational representation of social interaction suitable for implementation on a robot. An outcome matrix expresses the reward afforded to each interacting individual with respect to pairs of potential behaviors. We detail the use of the outcome matrix as a representation of interaction in social psychology and game theory, discuss the need for modeling the robot’s interactive partner, and contribute an algorithm for creating outcome matrices from perceptual information. Experimental results explore the use of the algorithm with different types of partners and in different environments.
The Coordination of Deliberative Reasoning in a Mobile Robot

(Georgia Institute of Technology, 2009) Ulam, Patrick D.

This paper examines the problem of how a mobile robot may coordinate among multiple, possibly conflicting deliberative processes for reasoning about object interactions in a soccer domain. This paper frames deliberative coordination as an instance of the algorithm selection problem and describes a novel framework by which a mobile robot may learn to coordinate its deliberative reasoning in response to constraints upon processing as well as the performance of each deliberative reasoner. Results of the framework are described for a simulated soccer task in which the robot must predict the motion of a fast moving ball in order to prevent it from reaching the goal area.
Lek Behavior as a Model for Multi-Robot Systems

(Georgia Institute of Technology, 2009-01-01) Duncan, Brittany A. ; Ulam, Patrick D. ; Arkin, Ronald C.

Lek behavior is a biological mechanism used by male birds to attract mates by forming a group. This project explores the use of a biological behavior found in many species of birds to form leks to guide the creation of groups of robots. The lek behavior provides a sound basis for multi-robot formation because it demonstrates a group of individual entities forming up around a scarce resource. This behavior can be useful to robots in many situations, with an example scenario the case in which robots were dropped via parachute into an area and then needed to form meaningful task-oriented groups.
Transferring Embodied Concepts Between Perceptually Heterogeneous Robots

(Georgia Institute of Technology, 2009) Kira, Zsolt

This paper explores methods and representations that allow two perceptually heterogeneous robots, each of which represents concepts via grounded properties, to transfer knowledge despite their differences. This is an important issue, as it will be increasingly important for robots to communicate and effectively share knowledge to speed up learning as they become more ubiquitous.We use Gӓrdenfors’ conceptual spaces to represent objects as a fuzzy combination of properties such as color and texture, where properties themselves are represented as Gaussian Mixture Models in a metric space. We then use confusion matrices that are built using instances from each robot, obtained in a shared context, in order to learn mappings between the properties of each robot. These mappings are then used to transfer a concept from one robot to another, where the receiving robot was not previously trained on instances of the objects. We show in a 3D simulation environment that these models can be successfully learned and concepts can be transferred between a ground robot and an aerial quadrotor robot.
Mapping Grounded Object Properties Across Perceptually Heterogeneous Embodiments

(Georgia Institute of Technology, 2009) Kira, Zsolt

As robots become more common, it becomes increasingly useful for them to communicate and effectively share knowledge that they have learned through their individual experiences. Learning from experiences, however, is oftentimes embodiment-specific; that is, the knowledge learned is grounded in the robot’s unique sensors and actuators. This type of learning raises questions as to how communication and knowledge exchange via social interaction can occur, as properties of the world can be grounded differently in different robots. This is especially true when the robots are heterogeneous, with different sensors and perceptual features used to define the properties. In this paper, we present methods and representations that allow heterogeneous robots to learn grounded property representations, such as that of color categories, and then build models of their similarities and differences in order to map their respective representations. We use a conceptual space representation, where object properties are learned and represented as regions in a metric space, implemented via supervised learning of Gaussian Mixture Models. We then propose to use confusion matrices that are built using instances from each robot, obtained in a shared context, in order to learn mappings between the properties of each robot. Results are demonstrated using two perceptually heterogeneous Pioneer robots, one with a web camera and another with a camcorder.
Beyond Humanoid Emotions: Incorporating Traits, Attitudes and Moods

(Georgia Institute of Technology, 2009) Moshkina, Lilia ; Arkin, Ronald C.

No longer does the idea of robot emotions seem far-fetched; not their experiential side, of course, but rather those manifestations of emotion, especially in robots created in human likeness, which would be beneficial for successful interaction with people. Nonetheless, the concept of robot emotions is still a new one, with a myriad of questions to be answered, not the least of which is: What is emotion? In robotics, it is often used as an umbrella term for all things affective, but based on our previous work (see [1] for a summary), we believe that it would be more beneficial to model each affective phenomenon explicitly. Going beyond emotions brings the entire spectrum of affect into play, providing a comprehensive framework with which human-robot interaction could be improved. The robotic framework we propose that combines a number of different phenomena and emphasizes their interconnectedness and synergy is called TAME (Traits, Attitudes, Moods, Emotions). By using TAME, in this paper we’d like to address some of the open questions that arise in the area of implementing and testing humanoid affect.
Lethality and Autonomous Systems: The Roboticist Demographic

(Georgia Institute of Technology, 2008) Moshkina, Lilia ; Arkin, Ronald C.

This paper reports the methods and results of an on-line survey addressing the issues surrounding lethality and autonomous systems that was conducted as part of a research project for the U.S. Army Research Office. The robotics researcher demographic, one of several targeted in this survey that includes policymakers, the military, and the general public, provides the data for this report. The design and administration of this survey and an analysis and discussion of the survey results are provided.
Robotic Discovery of the Auditory Scene

(Georgia Institute of Technology, 2007) Martinson, Eric ; Schultz, Alan

In this work, we describe an autonomous mobile robotic system for finding and investigating ambient noise sources in the environment. Motivated by the large negative effect of ambient noise sources on robot audition, the long-term goal is to provide awareness of the auditory scene to a robot, so that it may more effectively act to filter out the interference or re-position itself to increase the signal-to-noise ratio. Here, we concentrate on the discovery of new sources of sound through the use of mobility and directed investigation. This is performed in a two-step process. In the first step, a mobile robot first explores the surrounding acoustical environment, creating evidence grid representations to localize the most influential sound sources in the auditory scene. Then in the second step, the robot investigates each potential sound source location in the environment so as to improve the localization result, and identify volume and directionality characteristics of the sound source. Once every source has been investigated, a noise map of the entire auditory scene is created for use by the robot in avoiding areas of loud ambient noise when performing an auditory task.