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Arkin, Ronald C.

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Publication Search Results

Now showing 1 - 10 of 166
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    Formal Performance Guarantees for Behavior-Based Localization Missions
    (Georgia Institute of Technology, 2016-11) Lyons, Damian M. ; Arkin, Ronald C.
    Localization and mapping algorithms can allow a robot to navigate well in an unknown environment. However, whether such algorithms enhance any specific robot mission is currently a matter for empirical validation. In this paper we apply our MissionLab/VIPARS mission design and verification approach to an autonomous robot mission that uses probabilistic localization software. Two approaches to modeling probabilistic localization for verification are presented: a high-level approach, and a sample-based approach which allows run-time code to be embedded in verification. Verification and experimental validation results are presented for two waypoint missions using each method, demonstrating the accuracy of verification, and both are compared with verification of an odometry-only mission, to show the mission-specific benefit of localization.
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    An Analysis of Displays for Probabilistic Robotic Mission Verification Results
    (Georgia Institute of Technology, 2016) O‘Brien, Matthew ; Arkin, Ronald C.
    An approach for the verification of autonomous behavior-based robotic missions has been developed in a collaborative effort between Fordham University and Georgia Tech. This paper addresses the step after verification, how to present this information to users. The verification of robotic missions is inherently probabilistic, opening the possibility of misinterpretation by operators. A human study was performed to test three different displays (numeric, graphic, and symbolic) for summarizing the verification results. The displays varied by format and specificity. Participants made decisions about high-risk robotic missions using a prototype interface. Consistent with previous work, the type of display had no effect. The displays did not reduce the time participants took compared to a control group with no summary, but did improve the accuracy of their decisions. Participants showed a strong preference for more specific data, heavily using the full verification results. Based on these results, a different display paradigm is suggested.
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    Temporal Heterogeneity and the Value of Slowness in Robotic Systems
    (Georgia Institute of Technology, 2015-12) Arkin, Ronald C. ; Egerstedt, Magnus B.
    Robot teaming is a well-studied area, but little research to date has been conducted on the fundamental benefits of heterogeneous teams and virtually none on temporal heterogeneity, where timescales of the various platforms are radically different. This paper explores this aspect of robot ecosystems consisting of fast and slow robots (SlowBots) working together, including the bio-inspiration for such systems.
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    Temporal Heterogeneity and the Value of Slowness in Robotic Systems
    (Georgia Institute of Technology, 2015-12) Arkin, Ronald C. ; Egerstedt, Magnus B.
    Robot teaming is a well-studied area, but little research to date has been conducted on the fundamental benefits of heterogeneous teams and virtually none on temporal heterogeneity, where timescales of the various platforms are radically different. This paper explores this aspect of robot ecosystems consisting of fast and slow robots (SlowBots) working together, including the bio-inspiration for such systems.
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    Probabilistic Verification of Multi-robot Missions in Uncertain Environments
    (Georgia Institute of Technology, 2015-11) Lyons, Damian M. ; Arkin, Ronald C. ; Jiang, Shu ; Harrington, Dagan ; Tang, Feng ; Tang, Peng
    The effective use of autonomous robot teams in highly-critical missions depends on being able to establish performance guarantees. However, establishing a guarantee for the behavior of an autonomous robot operating in an uncertain environment with obstacles is a challenging problem. This paper addresses the challenges involved in building a software tool for verifying the behavior of a multi-robot waypoint mission that includes uncertain environment geometry as well as uncertainty in robot motion. One contribution of this paper is an approach to the problem of apriori specification of uncertain environments for robot program verification. A second contribution is a novel method to extend the Bayesian Network formulation to reason about random variables with different subpopulations, introduced to address the challenge of representing the effects of multiple sensory histories when verifying a robot mission. The third contribution is experimental validation results presented to show the effectiveness of this approach on a two-robot, bounding overwatch mission.
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    The Benefits of Robot Deception in Search and Rescue: Computational Approach for Deceptive Action Selection via Case-Based Reasoning
    (Georgia Institute of Technology, 2015-10) Shim, Jaeeun ; Arkin, Ronald C.
    By increasing the use of autonomous rescue robots in search and rescue (SAR), the chance of interaction between rescue robots and human victims also grows. More specifically, when autonomous rescue robots are considered in SAR, it is important for robots to handle sensitively human victims’ emotions. Deception can potentially be used effectively by robots to control human victims’ fear and shock as used by human rescuers. In this paper, we introduce robotic deception in SAR contexts and present a novel computational approach for an autonomous rescue robot’s deceptive action selection mechanism.
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    Mixed-Initiative Human-Robot Interaction: Definition, Taxonomy, and Survey
    (Georgia Institute of Technology, 2015-10) Jiang, Shu ; Arkin, Ronald C.
    The objectives of this article are: 1) to present a taxonomy for mixed-initiative human-robot interaction and 2) to survey its state of practice through the examination of past research along each taxonomical dimension. The paper starts with some definitions of mixed-initiative interaction (MII) from the perspective of human-computer interaction (HCI) to introduce the basic concepts of MII. We then synthesize these definitions to the robotic context for mixed-initiative human-robot teams. A taxonomy for mixed-initiative in human-robot interaction is then presented. The goal of the taxonomy is to inform the design of mixed-initiative human-robot systems by identifying key elements of these systems. The state of practice of mixed-initiative human-robot interaction is then surveyed and examined along each taxonomical dimension.
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    Towards a Robot Computational Model to Preserve Dignity in Stigmatizing Patient-Caregiver Relationships
    (Georgia Institute of Technology, 2015-10) Pettinati, Michael J. ; Arkin, Ronald C.
    Parkinson’s disease (PD) patients with an expressive mask are particularly vulnerable to stigmatization during interactions with their caregivers due to their inability to express affect through nonverbal channels. Our approach to uphold PD patient dignity is through the use of an ethical robot that mediates patient shame when it recognizes norm violations in the patient-caregiver interaction. This paper presents the basis for a computational model tasked with computing patient shame and the empathetic response of a caregiver during “empathetic opportunities” in their interaction. A PD patient is liable to suffer indignity when there is a substantial difference between his experienced shame and the empathy shown by the caregiver. When this difference falls outside of acceptable set bounds (norms), the robotic agent will act using subtle, nonverbal cues to guide the relationship back within these bounds, preserving patient dignity.
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    Primate-inspired Autonomous Navigation Using Mental Rotation and Advice-Giving
    (Georgia Institute of Technology, 2015-09) Velayudhan, Lakshmi ; Arkin, Ronald C.
    The cognitive process that enables many primate species to efficiently traverse their environment has been a subject of numerous studies. Mental rotation is hypothesized to be one such process. The evolutionary causes for dominance in primates of mental rotation over its counterpart, rotational invariance, is still not conclusively understood. Advice-giving offers a possible explanation for this dominance in more evolved primate species such as humans. This project aims at exploring the relationship between advice-giving and mental rotation by designing a system that combines the two processes in order to achieve successful navigation to a goal location. Two approaches to visual advice-giving were explored namely, segment based and object based advice-giving. The results obtained upon execution of the navigation algorithm on a Pioneer 2-DX robotic platform offers evidence regarding a linkage between advice-giving and mental rotation. An overall navigational accuracy of 90.9% and 71.43% were obtained respectively for the segment-based and object-based methods. These results also indicate how the two processes can function together in order to accomplish a navigational task in the absence of any external aid, as is the case with primates.
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    Performance Verification for Behavior-Based Robot Missions
    (Georgia Institute of Technology, 2015-06) Lyons, Damian M. ; Arkin, Ronald C. ; Jiang, Shu ; Liu, Tsung-Ming ; Nirmal, Paramesh
    Certain robot missions need to perform predictably in a physical environment that may have significant uncertainty. One approach is to leverage automatic software verification techniques to establish a performance guarantee. The addition of an environment model and uncertainty in both program and environment, however, means the state-space of a model-checking solution to the problem can be prohibitively large. An approach based on behavior-based controllers in a process-algebra framework that avoids state-space combinatorics is presented here. In this approach, verification of the robot program in the uncertain environment is reduced to a filtering problem for a Bayesian Network. Validation results are presented for the verification of a multiple-waypoint and an autonomous exploration robot mission.