19 research outputs found
Principles and Guidelines for Evaluating Social Robot Navigation Algorithms
A major challenge to deploying robots widely is navigation in human-populated
environments, commonly referred to as social robot navigation. While the field
of social navigation has advanced tremendously in recent years, the fair
evaluation of algorithms that tackle social navigation remains hard because it
involves not just robotic agents moving in static environments but also dynamic
human agents and their perceptions of the appropriateness of robot behavior. In
contrast, clear, repeatable, and accessible benchmarks have accelerated
progress in fields like computer vision, natural language processing and
traditional robot navigation by enabling researchers to fairly compare
algorithms, revealing limitations of existing solutions and illuminating
promising new directions. We believe the same approach can benefit social
navigation. In this paper, we pave the road towards common, widely accessible,
and repeatable benchmarking criteria to evaluate social robot navigation. Our
contributions include (a) a definition of a socially navigating robot as one
that respects the principles of safety, comfort, legibility, politeness, social
competency, agent understanding, proactivity, and responsiveness to context,
(b) guidelines for the use of metrics, development of scenarios, benchmarks,
datasets, and simulators to evaluate social navigation, and (c) a design of a
social navigation metrics framework to make it easier to compare results from
different simulators, robots and datasets.Comment: 43 pages, 11 figures, 6 table
The Seeing Eye Robot: Developing a Human-Aware Artificial Collaborator
Presented online via Bluejeans Events on October 27, 2021 at 12:15 p.m.Reuth Mirsky is a Postdoctoral fellow at the Computer Science Department in University of Texas at Austin. Her research interests include knowledge-driven methods like parsing, planning and knowledge-based diagnosis.Runtime: 50:52 minutesAutomated care systems are becoming more tangible than ever: recent breakthroughs in robotics and machine learning can be used to address the need for automated care created by the increasing aging population. However, such systems require overcoming several technological, ethical, and social challenges. One inspirational manifestation of these challenges can be observed in the training of seeing-eye dogs for visually impaired people. A seeing-eye dog is not just trained to obey its owner, but also to “intelligently disobey”: if it is given an unsafe command from its handler, it is taught to disobey it or even insist on a different course of action. This paper proposes the challenge of building a seeing-eye robot, as a thought-provoking use-case that helps identify the challenges to be faced when creating behaviors for robot assistants in general. Through this challenge, this paper delineates the prerequisites that an automated care system will need to have in order to perform intelligent disobedience and to serve as a true agent for its handler
The estimation of polyteny in Drosophila salivary gland nuclei based on determination of desoxyribonucleic acid content
Towards NeuroML: model description methods for collaborative modelling in neuroscience.
Biological nervous systems and the mechanisms underlying their operation exhibit astonishing complexity. Computational models of these systems have been correspondingly complex. As these models become ever more sophisticated, they become increasingly difficult to define, comprehend, manage and communicate. Consequently, for scientific understanding of biological nervous systems to progress, it is crucial for modellers to have software tools that support discussion, development and exchange of computational models. We describe methodologies that focus on these tasks, improving the ability of neuroscientists to engage in the modelling process. We report our findings on the requirements for these tools and discuss the use of declarative forms of model description--equivalent to object-oriented classes and database schema--which we call templates. We introduce NeuroML, a mark-up language for the neurosciences which is defined syntactically using templates, and its specific component intended as a common format for communication between modelling-related tools. Finally, we propose a template hierarchy for this modelling component of NeuroML, sufficient for describing models ranging in structural levels from neuron cell membranes to neural networks. These templates support both a framework for user-level interaction with models, and a high-performance framework for efficient simulation of the models