2,106 research outputs found
Closing the loop between neural network simulators and the OpenAI Gym
Since the enormous breakthroughs in machine learning over the last decade,
functional neural network models are of growing interest for many researchers
in the field of computational neuroscience. One major branch of research is
concerned with biologically plausible implementations of reinforcement
learning, with a variety of different models developed over the recent years.
However, most studies in this area are conducted with custom simulation scripts
and manually implemented tasks. This makes it hard for other researchers to
reproduce and build upon previous work and nearly impossible to compare the
performance of different learning architectures. In this work, we present a
novel approach to solve this problem, connecting benchmark tools from the field
of machine learning and state-of-the-art neural network simulators from
computational neuroscience. This toolchain enables researchers in both fields
to make use of well-tested high-performance simulation software supporting
biologically plausible neuron, synapse and network models and allows them to
evaluate and compare their approach on the basis of standardized environments
of varying complexity. We demonstrate the functionality of the toolchain by
implementing a neuronal actor-critic architecture for reinforcement learning in
the NEST simulator and successfully training it on two different environments
from the OpenAI Gym
A Model-Driven Engineering Approach for ROS using Ontological Semantics
This paper presents a novel ontology-driven software engineering approach for
the development of industrial robotics control software. It introduces the
ReApp architecture that synthesizes model-driven engineering with semantic
technologies to facilitate the development and reuse of ROS-based components
and applications. In ReApp, we show how different ontological classification
systems for hardware, software, and capabilities help developers in discovering
suitable software components for their tasks and in applying them correctly.
The proposed model-driven tooling enables developers to work at higher
abstraction levels and fosters automatic code generation. It is underpinned by
ontologies to minimize discontinuities in the development workflow, with an
integrated development environment presenting a seamless interface to the user.
First results show the viability and synergy of the selected approach when
searching for or developing software with reuse in mind.Comment: Presented at DSLRob 2015 (arXiv:1601.00877), Stefan Zander, Georg
Heppner, Georg Neugschwandtner, Ramez Awad, Marc Essinger and Nadia Ahmed: A
Model-Driven Engineering Approach for ROS using Ontological Semantic
Linking design and manufacturing domains via web-based and enterprise integration technologies
The manufacturing industry faces many challenges such as reducing time-to-market and cutting costs. In order to meet these increasing demands, effective methods are need to support the early product development stages by bridging the gap of communicating early design ideas and the evaluation of manufacturing performance. This paper introduces methods of linking design and manufacturing domains using disparate technologies. The combined technologies include knowledge management supporting for product lifecycle management (PLM) systems, enterprise resource planning (ERP) systems, aggregate process planning systems, workflow management and data exchange formats. A case study has been used to demonstrate the use of these technologies, illustrated by adding manufacturing knowledge to generate alternative early process plan which are in turn used by an ERP system to obtain and optimise a rough-cut capacity plan
Reducing the Barrier to Entry of Complex Robotic Software: a MoveIt! Case Study
Developing robot agnostic software frameworks involves synthesizing the
disparate fields of robotic theory and software engineering while
simultaneously accounting for a large variability in hardware designs and
control paradigms. As the capabilities of robotic software frameworks increase,
the setup difficulty and learning curve for new users also increase. If the
entry barriers for configuring and using the software on robots is too high,
even the most powerful of frameworks are useless. A growing need exists in
robotic software engineering to aid users in getting started with, and
customizing, the software framework as necessary for particular robotic
applications. In this paper a case study is presented for the best practices
found for lowering the barrier of entry in the MoveIt! framework, an
open-source tool for mobile manipulation in ROS, that allows users to 1)
quickly get basic motion planning functionality with minimal initial setup, 2)
automate its configuration and optimization, and 3) easily customize its
components. A graphical interface that assists the user in configuring MoveIt!
is the cornerstone of our approach, coupled with the use of an existing
standardized robot model for input, automatically generated robot-specific
configuration files, and a plugin-based architecture for extensibility. These
best practices are summarized into a set of barrier to entry design principles
applicable to other robotic software. The approaches for lowering the entry
barrier are evaluated by usage statistics, a user survey, and compared against
our design objectives for their effectiveness to users
Middleware platform for distributed applications incorporating robots, sensors and the cloud
Cyber-physical systems in the factory of the future
will consist of cloud-hosted software governing an agile
production process executed by autonomous mobile robots
and controlled by analyzing the data from a vast number of
sensors. CPSs thus operate on a distributed production floor
infrastructure and the set-up continuously changes with each
new manufacturing task. In this paper, we present our OSGibased
middleware that abstracts the deployment of servicebased
CPS software components on the underlying distributed
platform comprising robots, actuators, sensors and the cloud.
Moreover, our middleware provides specific support to develop
components based on artificial neural networks, a technique that
recently became very popular for sensor data analytics and robot
actuation. We demonstrate a system where a robot takes actions
based on the input from sensors in its vicinity
Experiences on a motivational learning approach for robotics in undergraduate courses
This paper presents an educational experience carried out in robotics undergraduate courses from two
different degrees: Computer Science and Industrial Engineering, having students with diverse
capabilities and motivations. The experience compares two learning strategies for the practical
lessons of such courses: one relies on code snippets in Matlab to cope with typical robotic problems
like robot motion, localization, and mapping, while the second strategy opts for using the ROS
framework for the development of algorithms facing a competitive challenge, e.g. exploration
algorithms. The obtained students’ opinions were instructive, reporting, for example, that although they
consider harder to master ROS when compared to Matlab, it might be more useful in their (robotic
related) professional careers, which enhanced their disposition to study it. They also considered that
the challenge-exercises, in addition to motivate them, helped to develop their skills as engineers to a
greater extent than the skeleton-code based ones. These and other conclusions will be useful in
posterior courses to boost the interest and motivation of the students.Universidad de Málaga. Campus de Excelencia Internacional AndalucĂa Tech
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Application of Machine Learning Methods to the Open-Loop Control of a Freeform Fabrication System
Freeform fabrication of complete functional devices requires the fabrication system to achieve well-controlled
deposition of many materials with widely varying material properties. In a research setting, material preparation
processes are not highly refined, causing batch property variation, and cost and time may prohibit accurate
quantification of the relevant material properties, such as viscosity, elasticity, etc. for each batch. Closed-loop
control based on the deposited material road is problematic due to the difficulty in non-contact measurement of the
road geometry, so a labor-intensive calibration and open-loop control method is typically used. In the present work,
k-Nearest Neighbor and Support Vector Machine (SVM) machine learning algorithms are applied to the problem of
generating open-loop control parameters which produce desired deposited material road geometry from a description
of a given material and tool configuration comprising a set of qualitative and quantitative attributes. Training data
for the algorithms is generated in the course of ordinary use of the SFF system as the results of manual calibration of
control parameters. Given the large instance space and the small training data set compiled thus far, the
performance is quite promising, although still insufficient to allow complete automation of the calibration process.
The SVM-based approach produces tolerable results when tested with materials not in the training data set. When
control parameters produced by the learning algorithms are used as a starting point for manual calibration,
significant operator time savings and material waste reduction may be achieved.Mechanical Engineerin
Modelling and analyzing adaptive self-assembling strategies with Maude
Building adaptive systems with predictable emergent behavior is a challenging task and it is becoming a critical need. The research community has accepted the challenge by introducing approaches of various nature: from software architectures, to programming paradigms, to analysis techniques. We recently proposed a conceptual framework for adaptation centered around the role of control data. In this paper we show that it can be naturally realized in a reflective logical language like Maude by using the Reflective Russian Dolls model. Moreover, we exploit this model to specify and analyse a prominent example of adaptive system: robot swarms equipped with obstacle-avoidance self-assembly strategies. The analysis exploits the statistical model checker PVesta
Constraint specification by example in a Meta-CASE tool
CASE tools are very helpful to software engineers in different ways and in different phases of software development. However, they are not easy to specialise to meet the needs of particular application domains or particular software modelling requirements. Meta-CASE tools offer a way of providing such specialisation by enabling a designer to specify a tool which is then generated automatically. Constraints are often used in such meta-CASE tools as a technique for governing the syntax and semantics of model elements and the values of their attributes. However, although constraint definition is a difficult process it has attracted relatively little research attention. The PhD research described here presents an approach for improving the process of CASE tool constraint specification based on the notion of programming by example (or demonstration). The feasibility of the approach will be demonstrated via experiments with a prototype using the meta-CASE tool Diagram Editor Constraints System (DECS) as context
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