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To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.robot.2005.09.028 Byline: Vandi Verma (a), Reid Simmons (b) Abstract: Experience has shown that even carefully designed and tested robots may encounter anomalous situations. It is therefore important for robots to monitor their state so that anomalous situations may be detected in a timely manner. Robot fault diagnosis typically requires tracking a very large number of possible faults in complex non-linear dynamic systems with noisy sensors. Traditional methods either ignore the uncertainty or use linear approximations of non-linear system dynamics. Such approximations are often unrealistic, and as a result faults either go undetected or become confused with non-fault conditions. Probability theory provides a natural representation for uncertainty, but an exact Bayesian solution for the diagnosis problem is intractable. Monte Carlo approximations have demonstrated considerable success in application domains such as computer vision and robot localization and mapping. But, classical Monte Carlo methods, such as particle filters, can suffer from substantial computational complexity. This is particularly true with the presence of rare, yet important events, such as many system faults. This paper presents an algorithm that provides an approach for computationally tractable fault diagnosis. Taking advantage of structure in the domain it dynamically concentrates computation in the regions of state space that are currently most relevant without losing track of less likely states. Experiments with a dynamic simulation of a six-wheel rocker-bogie rover show a significant improvement in performance over the classical approach. Author Affiliation: (a) MS 269-1, NASA Ames Research Center, Moffett Field, CA 94043, United States (b) Robotics Institute, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213, United States Article History: Received 18 January 2005; Accepted 13 September 2005

Autonomous robots frequently rely on models of their sensing and actions for intelligent decision making. Unfortunately, in complex environments, robots are bound to encounter situations in which their models do not accurately represent the world. Furthermore, these context-dependent model inaccuracies may be subtle, such that multiple observations may be necessary to distinguish them from noise. This paper formalizes the problem of detection and correction of such subtle contextual model inaccuracies in autonomous robots, and presents an algorithm to address this problem. The solution relies on reasoning about these contextual inaccuracies as parametric regions of inaccurate modeling (RIMs) in the robot's planning space. Empirical results from various real robot domains demonstrate that, by explicitly searching for RIMs, robots are capable of efficiently detecting subtle contextual model inaccuracies, which in turn can lead to task performance improvement. [ABSTRACT FROM AUTHOR]

This article presents the results of a study on the effects of representing time and intention in models of socially situated tasks on the quality of policies for robot behavior. The ability to reason about how others’ observable actions relate to their unobservable intentions is an important part of interpreting and responding to social behavior. It is also often necessary to observe the timing of actions in order to disambiguate others’ intentions. Therefore, our proposed approach is to model these interactions as time-indexed partially observable Markov decision processes (POMDPs). The intentions of humans are represented as hidden state in the POMDP models, and the time-dependence of actions by both humans and the robot are explicitly modelled. Our hypothesis is that planning for these interactions with a model that represents time dependent action outcomes and uncertainty about others’ intentions will achieve better results than simpler models that make fixed assumptions about people’s intentionality or abstract away time-dependent effects. A driving interaction governed by social conventions and involving ambiguity in the other driver’s intent was used as the scenario with which to test this hypothesis. A robot car controlled by policies from time-dependent POMDP models or by policies from two less expressive model variants performed this interaction in a driving simulator with human drivers. The time-dependent POMDP policies achieved better results than those of the models without explicit time representation or human intention as hidden state, both according to the reward obtained and to people’s subjective impressions of how socially appropriate and natural the robot’s behavior was. These results demonstrate both the relative superiority of these representation choices and the effectiveness of this approach to planning for socially situated tasks. [ABSTRACT FROM AUTHOR]

Recent research in human-robot interaction has investigated the concept of Sliding, or Adjustable, Autonomy, a mode of operation bridging the gap between explicit teleoperation and complete robot autonomy. This work has largely been in single-agent domains -- involving only one human and one robot -- and has not examined the issues that arise in multiagent domains. We discuss the issues involved in adapting Sliding Autonomy concepts to coordinated multiagent teams. In our approach, remote human operators have the ability to join, or leave, the team at will to assist the autonomous agents with their tasks (or aspects of their tasks) while not disrupting the team's coordination. Agents model their own and the human operator's performance on subtasks to enable them to determine when to request help from the operator. To validate our approach, we present the results of two experiments. The first evaluates the human/multirobot team's performance under four different collaboration strategies including complete teleoperation, pure autonomy, and two distinct versions of Sliding Autonomy. The second experiment compares a variety of user interface configurations to investigate how quickly a human operator can attain situational awareness when asked to help. The results of these studies support our belief that by incorporating a remote human operator into multiagent teams, the team as a whole becomes more robust and efficient. [ABSTRACT FROM AUTHOR]

We present in detail some of the challenges in developing reusable robotic software. We base that on our experience in developing the CLARAty robotics software, which is a generic object-oriented framework used for the integration of new algorithms in the areas of motion control, vision, manipulation, locomotion, navigation, localization, planning and execution. CLARAty was adapted to a number of heterogeneous robots with different mechanisms and hardware control architectures. In this paper, we also describe how we addressed some of these challenges in the development of the CLARAty software. [ABSTRACT FROM AUTHOR]

An introduction is presented in which the editor discusses various reports within the issue on topics including emphatic robots, interactions between humans and robots, and robots in social services.