Assigned responsibility for remote robot operation

The remote control of robots, known as teleoperation, is a non-trivial task, requiring the operator to make decisions based on the information relayed by the robot about its own status as well as its surroundings. This places the operator under significant cognitive load. A solution to this involves sharing this load between the human operator and automated operators. This paper builds on the idea of adjustable autonomy, proposing Assigned Responsibility, a way of clearly delimiting control responsibility over one or more robots between human and automated operators. An architecture for implementing Assigned Responsibility is presented

Adaptive planning for distributed systems using goal accomplishment tracking

Goal accomplishment tracking is the process of monitoring the progress of a task or series of tasks towards completing a goal. Goal accomplishment tracking is used to monitor goal progress in a variety of domains, including workflow processing, teleoperation and industrial manufacturing. Practically, it involves the constant monitoring of task execution, analysis of this data to determine the task progress and notification of interested parties. This information is usually used in a passive way to observe goal progress. However, responding to this information may prevent goal failures. In addition, responding proactively in an opportunistic way can also lead to goals being completed faster. This paper proposes an architecture to support the adaptive planning of tasks for fault tolerance or opportunistic task execution based on goal accomplishment tracking. It argues that dramatically increased performance can be gained by monitoring task execution and altering plans dynamically

Goal accomplishment tracking for automatic supervision of plan execution

It is common practice to break down plans into a series of goals or sub-goals in order to facilitate plan execution, thereby only burdening the individual agents responsible for their execution with small, easily achievable objectives at any one time, or providing a simple way of sharing these objectives amongst a group of these agents. Ensuring that plans are executed correctly is an essential part of any team management. To allow proper tracking of an agent's progress through a pre-planned set of goals, it is imperative to keep track of which of these goals have already been accomplished. This centralised approach is essential when the agent is part of a team of humans and/or robots, and goal accomplishment is not always being tracked at a low level. This paper presents a framework for an automated supervision system to keep track of changes in world states so as to chart progress through a pre-planned set of goals. An implementation of this framework on a mobile service robot is presented, and applied in an experiment which demonstrates its feasibility

Detecting periodicity in experimental data using linear modeling techniques

Fourier spectral estimates and, to a lesser extent, the autocorrelation function are the primary tools to detect periodicities in experimental data in the physical and biological sciences. We propose a new method which is more reliable than traditional techniques, and is able to make clear identification of periodic behavior when traditional techniques do not. This technique is based on an information theoretic reduction of linear (autoregressive) models so that only the essential features of an autoregressive model are retained. These models we call reduced autoregressive models (RARM). The essential features of reduced autoregressive models include any periodicity present in the data. We provide theoretical and numerical evidence from both experimental and artificial data, to demonstrate that this technique will reliably detect periodicities if and only if they are present in the data. There are strong information theoretic arguments to support the statement that RARM detects periodicities if they are present. Surrogate data techniques are used to ensure the converse. Furthermore, our calculations demonstrate that RARM is more robust, more accurate, and more sensitive, than traditional spectral techniques.Comment: 10 pages (revtex) and 6 figures. To appear in Phys Rev E. Modified styl

Stronger instruments via integer programming in an observational study of late preterm birth outcomes

In an optimal nonbipartite match, a single population is divided into matched pairs to minimize a total distance within matched pairs. Nonbipartite matching has been used to strengthen instrumental variables in observational studies of treatment effects, essentially by forming pairs that are similar in terms of covariates but very different in the strength of encouragement to accept the treatment. Optimal nonbipartite matching is typically done using network optimization techniques that can be quick, running in polynomial time, but these techniques limit the tools available for matching. Instead, we use integer programming techniques, thereby obtaining a wealth of new tools not previously available for nonbipartite matching, including fine and near-fine balance for several nominal variables, forced near balance on means and optimal subsetting. We illustrate the methods in our on-going study of outcomes of late-preterm births in California, that is, births of 34 to 36 weeks of gestation. Would lengthening the time in the hospital for such births reduce the frequency of rapid readmissions? A straightforward comparison of babies who stay for a shorter or longer time would be severely biased, because the principal reason for a long stay is some serious health problem. We need an instrument, something inconsequential and haphazard that encourages a shorter or a longer stay in the hospital. It turns out that babies born at certain times of day tend to stay overnight once with a shorter length of stay, whereas babies born at other times of day tend to stay overnight twice with a longer length of stay, and there is nothing particularly special about a baby who is born at 11:00 pm.Comment: Published in at http://dx.doi.org/10.1214/12-AOAS582 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Standardized field testing of assistant robots in a Mars-like environment

Controlled testing on standard tasks and within standard environments can provide meaningful performance comparisons between robots of heterogeneous design. But because they must perform practical tasks in unstructured, and therefore non-standard, environments, the benefits of this approach have barely begun to accrue for field robots. This work describes a desert trial of six student prototypes of astronaut-support robots using a set of standardized engineering tests developed by the US National Institute of Standards and Technology (NIST), along with three operational tests in natural Mars-like terrain. The results suggest that standards developed for emergency response robots are also applicable to the astronaut support domain, yielding useful insights into the differences in capabilities between robots and real design improvements. The exercise shows the value of combining repeatable engineering tests with task-specific application-testing in the field

Stabilizing the Retromer Complex in a Human Stem Cell Model of Alzheimer's Disease Reduces TAU Phosphorylation Independently of Amyloid Precursor Protein.

Developing effective therapeutics for complex diseases such as late-onset, sporadic Alzheimer's disease (SAD) is difficult due to genetic and environmental heterogeneity in the human population and the limitations of existing animal models. Here, we used hiPSC-derived neurons to test a compound that stabilizes the retromer, a highly conserved multiprotein assembly that plays a pivotal role in trafficking molecules through the endosomal network. Using this human-specific system, we have confirmed previous data generated in murine models and show that retromer stabilization has a potentially beneficial effect on amyloid beta generation from human stem cell-derived neurons. We further demonstrate that manipulation of retromer complex levels within neurons affects pathogenic TAU phosphorylation in an amyloid-independent manner. Taken together, our work demonstrates that retromer stabilization is a promising candidate for therapeutic development in AD and highlights the advantages of testing novel compounds in a human-specific, neuronal system

Dynamical modeling of collective behavior from pigeon flight data: flock cohesion and dispersion

Several models of flocking have been promoted based on simulations with qualitatively naturalistic behavior. In this paper we provide the first direct application of computational modeling methods to infer flocking behavior from experimental field data. We show that this approach is able to infer general rules for interaction, or lack of interaction, among members of a flock or, more generally, any community. Using experimental field measurements of homing pigeons in flight we demonstrate the existence of a basic distance dependent attraction/repulsion relationship and show that this rule is sufficient to explain collective behavior observed in nature. Positional data of individuals over time are used as input data to a computational algorithm capable of building complex nonlinear functions that can represent the system behavior. Topological nearest neighbor interactions are considered to characterize the components within this model. The efficacy of this method is demonstrated with simulated noisy data generated from the classical (two dimensional) Vicsek model. When applied to experimental data from homing pigeon flights we show that the more complex three dimensional models are capable of predicting and simulating trajectories, as well as exhibiting realistic collective dynamics. The simulations of the reconstructed models are used to extract properties of the collective behavior in pigeons, and how it is affected by changing the initial conditions of the system. Our results demonstrate that this approach may be applied to construct models capable of simulating trajectories and collective dynamics using experimental field measurements of herd movement. From these models, the behavior of the individual agents (animals) may be inferred