An Ethical Adaptor: Behavioral Modification Derived from Moral Emotions

This paper presents the motivation, basis and a prototype implementation of an ethical adaptor capable of using a moral affective function, guilt, as a basis for altering a robot’s ongoing behavior. While the research is illustrated in the context of the battlefield, the methods described are believed generalizable to other domains such as eldercare and are potentially extensible to a broader class of moral emotions, including compassion and empathy

Moral Decision-making in Autonomous Systems: Enforcement, Moral Emotions, Dignity, Trust and Deception

As humans are being progressively pushed further downstream in the decision-making process of autonomous systems, the need arises to ensure that moral standards, however defined, are adhered to by these robotic artifacts. While meaningful inroads have been made in this area regarding the use of ethical lethal military robots, including work by our laboratory, these needs transcend the warfighting domain and are pervasive, extending to eldercare, robot nannies, and other forms of service and entertainment robotic platforms. This paper presents an overview of the spectrum and specter of ethical issues raised by the advent of these systems, and various technical results obtained to date by our research group, geared towards managing ethical behavior in autonomous robots in relation to humanity. This includes: (1) the use of an ethical governor capable of restricting robotic behavior to predefined social norms; (2) an ethical adaptor which draws upon the moral emotions to allow a system to constructively and proactively modify its behavior based on the consequences of its actions; (3) the development of models of robotic trust in humans and its dual, deception, drawing on psychological models of interdependence theory; and (4) concluding with an approach towards the maintenance of dignity in human-robot relationships

Integrated Mission Specification and Task Allocation for Robot Teams - Design and Implementation

As the capabilities, range of missions, and the size of robot teams increase, the ability for a human operator to account for all the factors in these complex scenarios can become exceedingly difficult. Our previous research has studied the use of case-based reasoning (CBR) tools to assist a user in the generation of multi-robot missions. These tools, however, typically assume that the robots available for the mission are of the same type (i.e., homogeneous). We loosen this assumption through the integration of contract-net protocol (CNP) based task allocation coupled with a CBR-based mission specification wizard. Two alternative designs are explored for combining case-based mission specification and CNP-based team allocation as well as the tradeoffs that result from the selection of one of these approaches over the other

Multi-Robot User Interface Modeling

This paper investigates the problem of user interface design and evaluation for autonomous teams of heterogeneous mobile robots. We explore an operator modeling approach to multi-robot user interface evaluation. Specifically the authors generated GOMS models, a type of user model, to investigate potential interface problems and to guide the interface development process. Results indicate that our interface design changes improve the usability of multi-robot mission generation substantially. We conclude that modeling techniques such as GOMS can play an important role in robotic interface development. Moreover, this research indicates that these techniques can be performed in an inexpensive and timely manner, potentially reducing the need for costly and demanding usability studies

An Empirical Evaluation of Context-Sensitive Pose Estimators in an Urban Outdoor Environment

When a mobile robot is executing a navigational task in an urban outdoor environment, accurate localization information is often essential. The difficulty of this task is compounded by sensor drop-out and the presence of non-linear error sources over the span of the mission. We have observed that certain motions of the robot and environmental conditions affect pose sensors in different ways. In this paper, we propose a computational method for localization that systematically integrates and evaluates contextual information that affects the quality of sensors, and utilize the information in order to improve the output of sensor fusion. Our method was evaluated in comparison with conventional probabilistic localization methods (namely, the extended Kalman filter and Monte Carlo localization) in a set of outdoor experiments. The results of the experiment are also reported in this paper

Resonant nonlinear magneto-optical effects in atoms

In this article, we review the history, current status, physical mechanisms, experimental methods, and applications of nonlinear magneto-optical effects in atomic vapors. We begin by describing the pioneering work of Macaluso and Corbino over a century ago on linear magneto-optical effects (in which the properties of the medium do not depend on the light power) in the vicinity of atomic resonances, and contrast these effects with various nonlinear magneto-optical phenomena that have been studied both theoretically and experimentally since the late 1960s. In recent years, the field of nonlinear magneto-optics has experienced a revival of interest that has led to a number of developments, including the observation of ultra-narrow (1-Hz) magneto-optical resonances, applications in sensitive magnetometry, nonlinear magneto-optical tomography, and the possibility of a search for parity- and time-reversal-invariance violation in atoms.Comment: 51 pages, 23 figures, to appear in Rev. Mod. Phys. in Oct. 2002, Figure added, typos corrected, text edited for clarit

The Coordination of Deliberative Reasoning in a Mobile Robot

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

Niche Selection for Foraging Tasks in Multi-Robot Teams Using Reinforcement Learning

We present a means in which individual members of a multi-robot team may allocate themselves into specialist and generalist niches in a multi-foraging task where there may exist a cost for generalist strategies. Through the use of reinforcement learning, we show that the members can allocate themselves into effective distributions consistent with those distributions predicted by optimal foraging theory. These distributions are established without prior knowledge of the environment, without direct communication between team members, and with minimal state

Overriding Ethical Constraints in Lethal Autonomous Systems

This article describes the philosophy, design, and prototype implementation of an operator override system intended for use in managing unmanned robotic systems capable of lethal behavior. The ethical ramifications associated with the responsibility assignment of such a system are presented, which guide the development of the proof-of-concept system that serves as the basis for the simulation results presented herein

When Good Comms Go Bad: Communications Recovery for Multi-Robot Teams

Ad-hoc networks among groups of autonomous mobile robots are becoming a common occurrence as teams of robots take on increasingly complicated missions over wider areas. Research has often focused on proactive means in which the individual robots of the team may prevent communication failures between nodes in this network. This is not always possible especially in unknown or hostile environments. This research addresses reactive aspects of communication recovery. How should the members of the team react in the event of unseen communication failures between some or all of the nodes in the network? We present a number of behaviors to be utilized in the event of communications failure as well as a behavioral sequencer to further enhance the effectiveness of these recovery behaviors. The performance of the communication recovery behaviors is analyzed in simulation and their application on hardware platforms is discussed