12,762 research outputs found
Human-Machine Interface for Remote Training of Robot Tasks
Regardless of their industrial or research application, the streamlining of
robot operations is limited by the proximity of experienced users to the actual
hardware. Be it massive open online robotics courses, crowd-sourcing of robot
task training, or remote research on massive robot farms for machine learning,
the need to create an apt remote Human-Machine Interface is quite prevalent.
The paper at hand proposes a novel solution to the programming/training of
remote robots employing an intuitive and accurate user-interface which offers
all the benefits of working with real robots without imposing delays and
inefficiency. The system includes: a vision-based 3D hand detection and gesture
recognition subsystem, a simulated digital twin of a robot as visual feedback,
and the "remote" robot learning/executing trajectories using dynamic motion
primitives. Our results indicate that the system is a promising solution to the
problem of remote training of robot tasks.Comment: Accepted in IEEE International Conference on Imaging Systems and
Techniques - IST201
How a Diverse Research Ecosystem Has Generated New Rehabilitation Technologies: Review of NIDILRR’s Rehabilitation Engineering Research Centers
Over 50 million United States citizens (1 in 6 people in the US) have a developmental, acquired, or degenerative disability. The average US citizen can expect to live 20% of his or her life with a disability. Rehabilitation technologies play a major role in improving the quality of life for people with a disability, yet widespread and highly challenging needs remain. Within the US, a major effort aimed at the creation and evaluation of rehabilitation technology has been the Rehabilitation Engineering Research Centers (RERCs) sponsored by the National Institute on Disability, Independent Living, and Rehabilitation Research. As envisioned at their conception by a panel of the National Academy of Science in 1970, these centers were intended to take a “total approach to rehabilitation”, combining medicine, engineering, and related science, to improve the quality of life of individuals with a disability. Here, we review the scope, achievements, and ongoing projects of an unbiased sample of 19 currently active or recently terminated RERCs. Specifically, for each center, we briefly explain the needs it targets, summarize key historical advances, identify emerging innovations, and consider future directions. Our assessment from this review is that the RERC program indeed involves a multidisciplinary approach, with 36 professional fields involved, although 70% of research and development staff are in engineering fields, 23% in clinical fields, and only 7% in basic science fields; significantly, 11% of the professional staff have a disability related to their research. We observe that the RERC program has substantially diversified the scope of its work since the 1970’s, addressing more types of disabilities using more technologies, and, in particular, often now focusing on information technologies. RERC work also now often views users as integrated into an interdependent society through technologies that both people with and without disabilities co-use (such as the internet, wireless communication, and architecture). In addition, RERC research has evolved to view users as able at improving outcomes through learning, exercise, and plasticity (rather than being static), which can be optimally timed. We provide examples of rehabilitation technology innovation produced by the RERCs that illustrate this increasingly diversifying scope and evolving perspective. We conclude by discussing growth opportunities and possible future directions of the RERC program
Lifelong Federated Reinforcement Learning: A Learning Architecture for Navigation in Cloud Robotic Systems
This paper was motivated by the problem of how to make robots fuse and
transfer their experience so that they can effectively use prior knowledge and
quickly adapt to new environments. To address the problem, we present a
learning architecture for navigation in cloud robotic systems: Lifelong
Federated Reinforcement Learning (LFRL). In the work, We propose a knowledge
fusion algorithm for upgrading a shared model deployed on the cloud. Then,
effective transfer learning methods in LFRL are introduced. LFRL is consistent
with human cognitive science and fits well in cloud robotic systems.
Experiments show that LFRL greatly improves the efficiency of reinforcement
learning for robot navigation. The cloud robotic system deployment also shows
that LFRL is capable of fusing prior knowledge. In addition, we release a cloud
robotic navigation-learning website based on LFRL
Service-Oriented Architecture for Space Exploration Robotic Rover Systems
Currently, industrial sectors are transforming their business processes into
e-services and component-based architectures to build flexible, robust, and
scalable systems, and reduce integration-related maintenance and development
costs. Robotics is yet another promising and fast-growing industry that deals
with the creation of machines that operate in an autonomous fashion and serve
for various applications including space exploration, weaponry, laboratory
research, and manufacturing. It is in space exploration that the most common
type of robots is the planetary rover which moves across the surface of a
planet and conducts a thorough geological study of the celestial surface. This
type of rover system is still ad-hoc in that it incorporates its software into
its core hardware making the whole system cohesive, tightly-coupled, more
susceptible to shortcomings, less flexible, hard to be scaled and maintained,
and impossible to be adapted to other purposes. This paper proposes a
service-oriented architecture for space exploration robotic rover systems made
out of loosely-coupled and distributed web services. The proposed architecture
consists of three elementary tiers: the client tier that corresponds to the
actual rover; the server tier that corresponds to the web services; and the
middleware tier that corresponds to an Enterprise Service Bus which promotes
interoperability between the interconnected entities. The niche of this
architecture is that rover's software components are decoupled and isolated
from the rover's body and possibly deployed at a distant location. A
service-oriented architecture promotes integrate-ability, scalability,
reusability, maintainability, and interoperability for client-to-server
communication.Comment: LACSC - Lebanese Association for Computational Sciences,
http://www.lacsc.org/; International Journal of Science & Emerging
Technologies (IJSET), Vol. 3, No. 2, February 201
Overcoming barriers and increasing independence: service robots for elderly and disabled people
This paper discusses the potential for service robots to overcome barriers and increase independence of
elderly and disabled people. It includes a brief overview of the existing uses of service robots by disabled and elderly
people and advances in technology which will make new uses possible and provides suggestions for some of these new
applications. The paper also considers the design and other conditions to be met for user acceptance. It also discusses
the complementarity of assistive service robots and personal assistance and considers the types of applications and
users for which service robots are and are not suitable
ARTMAP-FTR: A Neural Network for Object Recognition Through Sonar on a Mobile Robot
ART (Adaptive Resonance Theory) neural networks for fast, stable learning and prediction have been applied in a variety of areas. Applications include automatic mapping from satellite remote sensing data, machine tool monitoring, medical prediction, digital circuit design, chemical analysis, and robot vision. Supervised ART architectures, called ARTMAP systems, feature internal control mechanisms that create stable recognition categories of optimal size by maximizing code compression while minimizing predictive error in an on-line setting. Special-purpose requirements of various application domains have led to a number of ARTMAP variants, including fuzzy ARTMAP, ART-EMAP, ARTMAP-IC, Gaussian ARTMAP, and distributed ARTMAP. A new ARTMAP variant, called ARTMAP-FTR (fusion target recognition), has been developed for the problem of multi-ping sonar target classification. The development data set, which lists sonar returns from underwater objects, was provided by the Naval Surface Warfare Center (NSWC) Coastal Systems Station (CSS), Dahlgren Division. The ARTMAP-FTR network has proven to be an effective tool for classifying objects from sonar returns. The system also provides a procedure for solving more general sensor fusion problems.Office of Naval Research (N00014-95-I-0409, N00014-95-I-0657
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