Sensitivity Analysis for Building Adaptive Robotic Software
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P. Jamshidi, M. Velez, C. Kästner, N. Siegmund, and P. Kawthekar. Transfer learning for improving model predictions in highly configurable software. Int’l Symp. Software Engineering for Adaptive and Self-Managing Systems (SEAMS), 2017.
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![SENSITIVITY ANALYSIS FOR
BUILDING ADAPTIVE ROBOTIC SOFTWARE
Pooyan Jamshidi, Miguel Velez and Christian Kästner
INTENT DISCOVERY: SENSITIVITY ANALYSIS FOR CONFIGURATION OPTIMIZATION
REDUCING COSTS WITH TRANSFER LEARNING
USE CASES
Systematic System Evolution
To automate or guide intelligent design choices.
Runtime Adaptation
To enable runtime adaptation of software configurations
to maintain quality of performance under dynamic
conditions (changing environment, goals, and tasks).
Performance Debugging
To guide robot software developers to identify potential
bugs causing low quality of performance.
RESULTS
Motivation:
Robotic software expose configurable parameters.
These tunable parameters affect performance of robots.
This can be leveraged to optimize performance.
Source Response Target Response
Transfer learning combines:
Lots of data gathered cheaply from
the simulator
With much less data gathered
expensively from the target robot
To make better predictions overall
PUBLICATIONS
P. Jamshidi, M. Velez, C. Kästner, N. Siegmund, and P. Kawthekar. Transfer
learning for improving model predictions in highly configurable software. Int’l
Symp. Software Engineering for Adaptive and Self-Managing Systems
(SEAMS), 2017.
P. Kawthekar and C. Kästner. Sensitivity analysis for building evolving &
adaptive robotic software, Workshop on Autonomous Mobile Service Robots
(WSF), 2016.
Predictive
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Simulator (Gazebo) Robot (TurtleBot)
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Using only a few real data points to
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Machine
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Localisation Error](https://image.slidesharecdn.com/poster-170321123313/85/Sensitivity-Analysis-for-Building-Adaptive-Robotic-Software-1-320.jpg)
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Software testing is an activity to enable a system is bug free during execution process. The software bug prediction is one of the most encouraging exercises of the testing phase of the software improvement life cycle. In any case, in this paper, a framework was created to anticipate the modules that deformity inclined in order to be utilized to all the more likely organize software quality affirmation exertion. Genetic Algorithm was used to extract relevant features from the acquired datasets to eliminate the possibility of overfitting and the relevant features were classified to defective or otherwise modules using the Artificial Neural Network. The system was executed in MATLAB (R2018a) Runtime environment utilizing a statistical toolkit and the performance of the system was assessed dependent on the accuracy, precision, recall, and the f-score to check the effectiveness of the system. In the finish of the led explores, the outcome indicated that ECLIPSE JDT CORE, ECLIPSE PDE UI, EQUINOX FRAMEWORK and LUCENE has the accuracy, precision, recall and the f-score of 86.93, 53.49, 79.31 and 63.89% respectively, 83.28, 31.91, 45.45 and 37.50% respectively, 83.43, 57.69, 45.45 and 50.84% respectively and 91.30, 33.33, 50.00 and 40.00% respectively. This paper presents an improved software predictive system for the software defect detections.
2cee Master Cocomo20071
21st century effort estimation methods from JPL. Jairus Hihn (JPL), Tim Menzies (WVU), Karen Lum (JPL), Dan Baker (WVU)
IJERD (www.ijerd.com) International Journal of Engineering Research and Devel...
The document presents a hybrid neural network model using particle swarm optimization (PSO) to evaluate the quality of object-oriented software modules by predicting fault-prone components. The model trains a neural network using PSO on 80% of a dataset containing code attributes from NASA projects. It then tests the trained network on the remaining 20% and calculates accuracy, mean absolute error, and root mean squared error at different iterations, showing improved results as iterations increase. Compared to other methods, the PSO-trained neural network achieves higher accuracy and lower errors in fault prediction.
A Machine learning based framework for Verification and Validation of Massive...
This document presents a machine learning based framework for verification and validation of massive scale image data. It discusses the challenges of managing and analyzing large image datasets. The proposed framework uses techniques like data augmentation, feature extraction and selection, decision trees, cross-validation and test cases to systematically manage massive image data and validate machine learning algorithms and systems. It uses Cell Morphology Analysis (CMA) as a case study to demonstrate how the framework can verify and validate large datasets, software systems and algorithms. The effectiveness of the framework is shown through its application to CMA, which involves classifying cell images using machine learning.
Trends in Embedded Software Engineering
This document summarizes trends in embedded software engineering, including moving from traditional coding to model-driven engineering and domain-specific development. It also discusses quality assurance techniques for safety-critical systems, such as static and formal verification as well as dynamic testing. Model-based approaches like fault tree analysis and measurement-based reliability growth models are presented for safety and reliability analysis. Overall, the document outlines challenges in developing complex embedded systems and the need for continued advances in systematic engineering technologies.
Towards a Macrobenchmark Framework for Performance Analysis of Java Applications
This document discusses the need for macrobenchmarks to evaluate the performance and scalability of large model querying systems. It presents the Train Benchmark, which measures the performance of validation queries on randomly generated railway network models of increasing sizes. The benchmark includes loading models, running validation queries to detect errors, transforming models by injecting faults, and revalidating. It aims to provide a realistic and scalable way to assess model querying tools for domains like software engineering, where models can contain billions of elements.
Software performance simulation strategies for high-level embedded system design
This document discusses software performance simulation strategies for high-level embedded system design. It introduces instruction set simulators (ISSs), which are accurate but slow. It also discusses binary-level simulation (BLS) and source-level simulation (SLS), which are faster approaches based on native execution. The document proposes a new approach called intermediate source code instrumentation based simulation (iSciSim) that aims to achieve high accuracy, speed, and low complexity for system-level design space exploration.
A Runtime Evaluation Methodology and Framework for Autonomic Systems
An autonomic system provides self-adaptive ability
that enables system to dynamically adjust its behavior on
environmental changes or system failure. Fundamental
process of adaptive behavior in an autonomic system is consist
of monitoring system or/and environment information,
analyzing monitored information, planning adaptation policy
and executing selected policy. Evaluating system utility is
one of a significant part among them. We propose a novel
approach on evaluating autonomic system at runtime. Our
proposed method takes advantage of a goal model that has
been widely used at requirement elicitation phase to capture
system requirements. We suggest the state-based goal model
that is dynamically activated as the system state changes. In
addition, we defined type of constraints that can be used to
evaluate goal satisfaction level. We implemented a prototype
of autonomic computing software engine to verity our proposed
method. We simulated the behavior of the autonomic
computing engine with the home surveillance robot scenario
and observed the validity of our proposed method
Similar to Sensitivity Analysis for Building Adaptive Robotic Software (20)
A Novel Approach to Improve Software Defect Prediction Accuracy Using Machine...
A Novel Approach to Improve Software Defect Prediction Accuracy Using Machine...
Effective Software Effort Estimation Leveraging Machine Learning for Digital ...
Effective Software Effort Estimation Leveraging Machine Learning for Digital ...
Modelling Open-Source Software Reliability Incorporating Swarm Intelligence-B...
Modelling Open-Source Software Reliability Incorporating Swarm Intelligence-B...
implementing_ai_for_improved_performance_testing_the_key_to_success.pptx
implementing_ai_for_improved_performance_testing_the_key_to_success.pptx
A Defect Prediction Model for Software Product based on ANFIS
A Defect Prediction Model for Software Product based on ANFIS
A Defect Prediction Model for Software Product based on ANFIS
A Defect Prediction Model for Software Product based on ANFIS
Development of software defect prediction system using artificial neural network
Development of software defect prediction system using artificial neural network
IJERD (www.ijerd.com) International Journal of Engineering Research and Devel...
IJERD (www.ijerd.com) International Journal of Engineering Research and Devel...
A Machine learning based framework for Verification and Validation of Massive...
A Machine learning based framework for Verification and Validation of Massive...
Towards a Macrobenchmark Framework for Performance Analysis of Java Applications
Towards a Macrobenchmark Framework for Performance Analysis of Java Applications
Software performance simulation strategies for high-level embedded system design
Software performance simulation strategies for high-level embedded system design
A Runtime Evaluation Methodology and Framework for Autonomic Systems
A Runtime Evaluation Methodology and Framework for Autonomic Systems
More from Pooyan Jamshidi
Learning LWF Chain Graphs: A Markov Blanket Discovery Approach
LWF Chain graphs were introduced by Lauritzen, Wermuth, and Frydenberg as a generalization of graphical models based on undirected graphs and DAGs. From the causality point of view, in an LWF CG: Directed edges represent direct causal effects. Undirected edges represent causal effects due to interference, which occurs when an individual’s outcome is influenced by their social interaction with other population members, e.g., in situations that involve contagious agents, educational programs, or social networks. The construction of chain graph models is a challenging task that would be greatly facilitated by automation.
Markov blanket discovery has an important role in structure learning of Bayesian network. It is surprising, however, how little attention it has attracted in the context of learning LWF chain graphs. In this work, we provide a graphical characterization of Markov blankets in chain graphs. The characterization is different from the well-known one for Bayesian networks and generalizes it. We provide a novel scalable and sound algorithm for Markov blanket discovery in LWF chain graphs. We also provide a sound and scalable constraint-based framework for learning the structure of LWF CGs from faithful causally sufficient data. With the use of our algorithm, the problem of structure learning is reduced to finding an efficient algorithm for Markov blanket discovery in LWF chain graphs. This greatly simplifies the structure-learning task and makes a wide range of inference/learning problems computationally tractable because our approach exploits locality.
A Framework for Robust Control of Uncertainty in Self-Adaptive Software Conn...
We enable reliable and dependable self‐adaptations of component connectors in unreliable environments with imperfect monitoring facilities and conflicting user opinions about adaptation policies by developing a framework which comprises: (a) mechanisms for robust model evolution, (b) a method for adaptation reasoning, and (c) tool support that allows an end‐to‐end application of the developed techniques in real‐world domains.
Machine Learning Meets Quantitative Planning: Enabling Self-Adaptation in Aut...
Modern cyber-physical systems (e.g., robotics systems) are typically composed of physical and software components, the characteristics of which are likely to change over time. Assumptions about parts of the system made at design time may not hold at run time, especially when a system is deployed for long periods (e.g., over decades). Self-adaptation is designed to find reconfigurations of systems to handle such run-time inconsistencies. Planners can be used to find and enact optimal reconfigurations in such an evolving context. However, for systems that are highly configurable, such planning becomes intractable due to the size of the adaptation space. To overcome this challenge, in this paper we explore an approach that (a) uses machine learning to find Pareto-optimal configurations without needing to explore every configuration and (b) restricts the search space to such configurations to make planning tractable. We explore this in the context of robot missions that need to consider task timeliness and energy consumption. An independent evaluation shows that our approach results in high-quality adaptation plans in uncertain and adversarial environments.
Paper: https://arxiv.org/abs/1903.03920
Ensembles of Many Diverse Weak Defenses can be Strong: Defending Deep Neural ...
Despite achieving state-of-the-art performance across many domains, machine learning systems are highly vulnerable to subtle adversarial perturbations. Although defense approaches have been proposed in recent years, many have been bypassed by even weak adversarial attacks. Previous studies showed that ensembles created by combining multiple weak defenses (i.e., input data transformations) are still weak. In this talk, I will show that it is indeed possible to construct effective ensembles using weak defenses to block adversarial attacks. However, to do so requires a diverse set of such weak defenses. Based on this motivation, I will present Athena, an extensible framework for building effective defenses to adversarial attacks against machine learning systems. I will talk about the effectiveness of ensemble strategies with a diverse set of many weak defenses that comprise transforming the inputs (e.g., rotation, shifting, noising, denoising, and many more) before feeding them to target deep neural network classifiers. I will also discuss the effectiveness of the ensembles with adversarial examples generated by various adversaries in different threat models. In the second half of the talk, I will explain why building defenses based on the idea of many diverse weak defenses works, when it is most effective, and what its inherent limitations and overhead are.
Transfer Learning for Performance Analysis of Machine Learning Systems
This document discusses transfer learning approaches for analyzing the performance of machine learning systems. It begins with the presenter's background and credentials. It then notes that today's most popular systems are highly configurable, but understanding how configurations impact performance is challenging. The document uses a case study of a social media analytics system called SocialSensor to illustrate the opportunity of exploring different configurations to improve performance without extra resources. Testing various configurations of SocialSensor's data processing pipelines revealed that the default was suboptimal, and an optimal configuration found through experimentation significantly outperformed the default and an expert's recommendation. The document concludes that default configurations are often bad, but transfer learning approaches can help identify configurations that noticeably improve performance.
Transfer Learning for Performance Analysis of Configurable Systems: A Causal ...
Modern systems (e.g., deep neural networks, big data analytics, and compilers) are highly configurable, which means they expose different performance behavior under different configurations. The fundamental challenge is that one cannot simply measure all configurations due to the sheer size of the configuration space. Transfer learning has been used to reduce the measurement efforts by transferring knowledge about performance behavior of systems across environments. Previously, research has shown that statistical models are indeed transferable across environments. In this work, we investigate identifiability and transportability of causal effects and statistical relations in highly-configurable systems. Our causal analysis agrees with previous exploratory analysis~\cite{Jamshidi17} and confirms that the causal effects of configuration options can be carried over across environments with high confidence. We expect that the ability to carry over causal relations will enable effective performance analysis of highly-configurable systems.
Machine Learning meets DevOps
1) Machine learning systems are increasingly configurable, making their performance behavior complex and difficult to understand. 2) The document discusses a social media monitoring system called SocialSensor, which uses a configurable data processing pipeline. 3) By exploring different system configurations, the performance of SocialSensor could potentially be improved without requiring more resources. This demonstrates an opportunity to optimize performance through configuration tuning.
Learning to Sample
Learning to Sample: Exploiting Similarities across Environments to Learn Performance Models for Configurable Systems @ FSE'18
Integrated Model Discovery and Self-Adaptation of Robots
Machine learn models efficiently under budget constraints to adapt to perturbations such as environmental changes or changes in the internal resources.
Modern software-intensive systems are composed of components that are likely to change their behaviour over time (e.g., adding/removing components).
For software to continue to operate under such changes, the assumptions about parts of the system made at design time may not hold at runtime due to uncertainty.
Mechanisms must be put in place that can dynamically learn new models of these assumptions and use them to make decisions about missions, configurations, etc.
Transfer Learning for Performance Analysis of Highly-Configurable Software
This document discusses using transfer learning to analyze the performance of configurable software systems. It begins by noting that today's most popular software systems are highly configurable and that their increasing configurability makes understanding performance behavior difficult. The author then describes using transfer learning to enable learning performance models more efficiently by reusing data from related source domains. This allows developers and users to better understand performance tradeoffs and find optimal configurations.
Architectural Tradeoff in Learning-Based Software
In classical software development, developers write explicit instructions in a programming language to hardcode the explicit behavior of software systems. By writing each line of code, the programmer instructs the software to have the desirable behavior by exploring a specific point in program space.
Recently, however, software systems are adding learning components that, instead of hardcoding an explicit behavior, learn a behavior through data. The learning-intensive software systems are written in terms of models and their parameters that need to be adjusted based on data. In learning-enabled systems, we specify some constraints on the behavior of a desirable program (e.g., a data set of input–output pairs of examples) and use the computational resources to search through the program space to find a program that satisfies the constraints. In neural networks, we restrict the search to a continuous subset of the program space.
This talk provides experimental evidence of making tradeoffs for deep neural network models, using the Deep Neural Network Architecture system as a case study. Concrete experimental results are presented; also featured are additional case studies in big data (Storm, Cassandra), data analytics (configurable boosting algorithms), and robotics applications.
Production-Ready Machine Learning for the Software Architect
This document summarizes a guest lecture on building production-ready machine learning systems. The lecturer discusses how a startup called Sniffable, which created an Instagram-like app for dogs, tried to build a machine learning system called Pooch Predictor to predict how popular photos would be. However, Pooch Predictor failed repeatedly in production due to issues like not retraining models, tight coupling between components, and treating ML like a transactional rather than dynamic system. The lecturer emphasizes that ML systems must be reactive, responsive, resilient, elastic, and message-driven to succeed at scale.
Transfer Learning for Software Performance Analysis: An Exploratory Analysis
The document discusses transfer learning for building performance models of configurable software systems. Building accurate performance models through direct measurement is challenging due to the large configuration space and environmental factors. Transfer learning aims to address this by leveraging knowledge from performance models built for related systems or environments to improve the learning process for new systems and environments. The goal is to develop techniques that allow predicting and optimizing performance for configurable systems across changing environments.
Architecting for Scale
Guest lecture talk in the CMU's Foundation of Software Engineering course: http://www.cs.cmu.edu/~ckaestne/15313/2017/
Learning Software Performance Models for Dynamic and Uncertain Environments
This document provides background on Pooyan Jamshidi's research related to learning software performance models for dynamic and uncertain environments. It summarizes his past work developing techniques for modeling and optimizing performance across different systems and environments, including using transfer learning to reuse performance data from related sources to build more accurate models with fewer measurements. It also outlines opportunities for using transfer learning to adapt performance models to new environments and systems.
Machine Learning meets DevOps
The document discusses using machine learning techniques like Gaussian processes (GPs) to optimize the configuration of software systems. It notes that software performance landscapes are often complex, with non-linear interactions between parameters and non-convex response surfaces. Measurements are also subject to noise. The document introduces an approach called TL4CO that uses multi-task Gaussian processes to model software performance across different versions/deployments, allowing it to leverage data from other versions to improve optimization. This helps address challenges in DevOps where new versions are continuously delivered.
Fuzzy Self-Learning Controllers for Elasticity Management in Dynamic Cloud Ar...
(1) The document discusses challenges in managing elasticity in cloud architectures due to unpredictable demand and uncertainty in measurements. (2) It proposes a fuzzy self-learning controller called RobusT2Scale that uses type-2 fuzzy logic to qualitatively specify thresholds and make robust scaling decisions despite uncertainty. (3) Experimental results show that RobusT2Scale is able to guarantee service level agreements while avoiding over- and under-provisioning of resources compared to other approaches.
Configuration Optimization for Big Data Software
The document discusses configuration optimization for big data software using an approach developed in the DICE project funded by the European Union's Horizon 2020 program. It describes optimizing configurations for Apache Storm and Cassandra to significantly reduce configuration time. Experiments showed large performance variations between configurations and that default settings often performed poorly compared to optimized settings. Tuning on one version did not guarantee good performance on other versions, but transferring more observations from other versions improved performance, though with diminishing returns due to increased optimization costs.
Microservices Architecture Enables DevOps: Migration to a Cloud-Native Archit...
A look at the searches related to the term “microservices” on Google Trends revealed that the top searches are now technology driven. This implies that the time of general search terms such as “What is microservices?” has now long passed. Not only are software vendors (for example, IBM and Microsoft) using microservices and DevOps practices, but also content providers (for example, Netflix and the BBC) have adopted and are using them.
I report on experiences and lessons learned during incremental migration and architectural refactoring of a commercial mobile back end as a service to microservices architecture. I explain how we adopted DevOps and how this facilitated a smooth migration towards Microservices architecture.
Self learning cloud controllers
This document discusses self-learning cloud controllers that can dynamically scale cloud resources. It notes that current auto-scaling approaches require deep application knowledge and expertise to determine scaling parameters and policies. The paper proposes a type-2 fuzzy logic approach called RobusT2Scale that uses fuzzy rules and monitoring data to determine scaling actions. It aims to handle uncertainty in elastic systems and accommodate different user preferences through fuzzy reasoning over workload and response time data. The approach pre-computes scaling decisions to enable efficient runtime elasticity control. It is evaluated based on its ability to meet an SLA target response time compared to over- and under-provisioning approaches.
More from Pooyan Jamshidi (20)
Learning LWF Chain Graphs: A Markov Blanket Discovery Approach
Learning LWF Chain Graphs: A Markov Blanket Discovery Approach
A Framework for Robust Control of Uncertainty in Self-Adaptive Software Conn...
A Framework for Robust Control of Uncertainty in Self-Adaptive Software Conn...
Machine Learning Meets Quantitative Planning: Enabling Self-Adaptation in Aut...
Machine Learning Meets Quantitative Planning: Enabling Self-Adaptation in Aut...
Ensembles of Many Diverse Weak Defenses can be Strong: Defending Deep Neural ...
Ensembles of Many Diverse Weak Defenses can be Strong: Defending Deep Neural ...
Transfer Learning for Performance Analysis of Machine Learning Systems
Transfer Learning for Performance Analysis of Machine Learning Systems
Transfer Learning for Performance Analysis of Configurable Systems: A Causal ...
Transfer Learning for Performance Analysis of Configurable Systems: A Causal ...
Integrated Model Discovery and Self-Adaptation of Robots
Integrated Model Discovery and Self-Adaptation of Robots
Transfer Learning for Performance Analysis of Highly-Configurable Software
Transfer Learning for Performance Analysis of Highly-Configurable Software
Production-Ready Machine Learning for the Software Architect
Production-Ready Machine Learning for the Software Architect
Transfer Learning for Software Performance Analysis: An Exploratory Analysis
Transfer Learning for Software Performance Analysis: An Exploratory Analysis
Learning Software Performance Models for Dynamic and Uncertain Environments
Learning Software Performance Models for Dynamic and Uncertain Environments
Fuzzy Self-Learning Controllers for Elasticity Management in Dynamic Cloud Ar...
Fuzzy Self-Learning Controllers for Elasticity Management in Dynamic Cloud Ar...
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Microservices Architecture Enables DevOps: Migration to a Cloud-Native Archit...
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HERE IS YOUR WEBINAR CONTENT! 'Mastering Customer Journey Management with Dr. Graham Hill'. We hope you find the webinar recording both insightful and enjoyable.
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This slide deck presents DLHT, a concurrent in-memory hashtable. Despite efforts to optimize hashtables, that go as far as sacrificing core functionality, state-of-the-art designs still incur multiple memory accesses per request and block request processing in three cases. First, most hashtables block while waiting for data to be retrieved from memory. Second, open-addressing designs, which represent the current state-of-the-art, either cannot free index slots on deletes or must block all requests to do so. Third, index resizes block every request until all objects are copied to the new index. Defying folklore wisdom, DLHT forgoes open-addressing and adopts a fully-featured and memory-aware closed-addressing design based on bounded cache-line-chaining. This design offers lock-free index operations and deletes that free slots instantly, (2) completes most requests with a single memory access, (3) utilizes software prefetching to hide memory latencies, and (4) employs a novel non-blocking and parallel resizing. In a commodity server and a memory-resident workload, DLHT surpasses 1.6B requests per second and provides 3.5x (12x) the throughput of the state-of-the-art closed-addressing (open-addressing) resizable hashtable on Gets (Deletes).
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Speaker: Deepak Mahto, Founder of DataCloudGaze Consulting
Date & Time: 8th June | 10 AM - 1 PM IST
Venue: Bangalore International Centre, Bangalore
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Contact us: info@mydbops.com
Visit: https://www.mydbops.com/
Follow us on LinkedIn: https://in.linkedin.com/company/mydbops
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Demystifying Knowledge Management through Storytelling
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Introducing BoxLang : A new JVM language for productivity and modularity!
Just like life, our code must adapt to the ever changing world we live in. From one day coding for the web, to the next for our tablets or APIs or for running serverless applications. Multi-runtime development is the future of coding, the future is to be dynamic. Let us introduce you to BoxLang.
Dynamic. Modular. Productive.
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Interoperability at its Core
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- 1. SENSITIVITY ANALYSIS FOR BUILDING ADAPTIVE ROBOTIC SOFTWARE Pooyan Jamshidi, Miguel Velez and Christian Kästner INTENT DISCOVERY: SENSITIVITY ANALYSIS FOR CONFIGURATION OPTIMIZATION REDUCING COSTS WITH TRANSFER LEARNING USE CASES Systematic System Evolution To automate or guide intelligent design choices. Runtime Adaptation To enable runtime adaptation of software configurations to maintain quality of performance under dynamic conditions (changing environment, goals, and tasks). Performance Debugging To guide robot software developers to identify potential bugs causing low quality of performance. RESULTS Motivation: Robotic software expose configurable parameters. These tunable parameters affect performance of robots. This can be leveraged to optimize performance. Source Response Target Response Transfer learning combines: Lots of data gathered cheaply from the simulator With much less data gathered expensively from the target robot To make better predictions overall PUBLICATIONS P. Jamshidi, M. Velez, C. Kästner, N. Siegmund, and P. Kawthekar. Transfer learning for improving model predictions in highly configurable software. Int’l Symp. Software Engineering for Adaptive and Self-Managing Systems (SEAMS), 2017. P. Kawthekar and C. Kästner. Sensitivity analysis for building evolving & adaptive robotic software, Workshop on Autonomous Mobile Service Robots (WSF), 2016. Predictive Model Learn Model Measure Measure Data Source Target Simulator (Gazebo) Robot (TurtleBot) Predict Performance Predictions Adaptation Use for analysis 5 10 15 20 25 number of particles 5 10 15 20 25 numberofrefinements 0 5 10 15 20 25 5 10 15 20 25 number of particles 5 10 15 20 25 numberofrefinements 0 5 10 15 20 25 5 10 15 20 25 number of particles 5 10 15 20 25 numberofrefinements 0 5 10 15 20 25 CPU usage [%] CPU usage [%] (a) (b) (c) (d) Prediction without transfer learning 5 10 15 20 25 5 10 15 20 25 10 15 20 25 Prediction with transfer learning Using only a few real data points to predict yields poor results across configuration space Using transfer learning to combine the few real data points with lots of approximate data yields a good model Machine Learning Configuration Parameters Design of Experiment Configuration Space Predictive Model Sensitivity Analysis DataMeasurem ents Configuration Space Data Accuracy Energy CPU 0 5 10 15 20 25 30 35 mean CPU utilization 0 500 1000 1500 2000 2500 3000 3500 numberofconfigurations 5 10 15 20 25 number of particles 5 10 15 20 25 numberofrefinements 6 8 10 12 14 16 18 20 22 24 26 5 10 15 20 25 number of particles 5 10 15 20 25 numberofrefinements 5 10 15 20 25 30 35 40 45 CPU Localisation Error