42,016 research outputs found
Evaluating Model Testing and Model Checking for Finding Requirements Violations in Simulink Models
Matlab/Simulink is a development and simulation language that is widely used
by the Cyber-Physical System (CPS) industry to model dynamical systems. There
are two mainstream approaches to verify CPS Simulink models: model testing that
attempts to identify failures in models by executing them for a number of
sampled test inputs, and model checking that attempts to exhaustively check the
correctness of models against some given formal properties. In this paper, we
present an industrial Simulink model benchmark, provide a categorization of
different model types in the benchmark, describe the recurring logical patterns
in the model requirements, and discuss the results of applying model checking
and model testing approaches to identify requirements violations in the
benchmarked models. Based on the results, we discuss the strengths and
weaknesses of model testing and model checking. Our results further suggest
that model checking and model testing are complementary and by combining them,
we can significantly enhance the capabilities of each of these approaches
individually. We conclude by providing guidelines as to how the two approaches
can be best applied together.Comment: 10 pages + 2 page reference
Deep Reinforcement Learning for Tensegrity Robot Locomotion
Tensegrity robots, composed of rigid rods connected by elastic cables, have a
number of unique properties that make them appealing for use as planetary
exploration rovers. However, control of tensegrity robots remains a difficult
problem due to their unusual structures and complex dynamics. In this work, we
show how locomotion gaits can be learned automatically using a novel extension
of mirror descent guided policy search (MDGPS) applied to periodic locomotion
movements, and we demonstrate the effectiveness of our approach on tensegrity
robot locomotion. We evaluate our method with real-world and simulated
experiments on the SUPERball tensegrity robot, showing that the learned
policies generalize to changes in system parameters, unreliable sensor
measurements, and variation in environmental conditions, including varied
terrains and a range of different gravities. Our experiments demonstrate that
our method not only learns fast, power-efficient feedback policies for rolling
gaits, but that these policies can succeed with only the limited onboard
sensing provided by SUPERball's accelerometers. We compare the learned feedback
policies to learned open-loop policies and hand-engineered controllers, and
demonstrate that the learned policy enables the first continuous, reliable
locomotion gait for the real SUPERball robot. Our code and other supplementary
materials are available from http://rll.berkeley.edu/drl_tensegrityComment: International Conference on Robotics and Automation (ICRA), 2017.
Project website link is http://rll.berkeley.edu/drl_tensegrit
Probabilistic Guarantees for Safe Deep Reinforcement Learning
Deep reinforcement learning has been successfully applied to many control
tasks, but the application of such agents in safety-critical scenarios has been
limited due to safety concerns. Rigorous testing of these controllers is
challenging, particularly when they operate in probabilistic environments due
to, for example, hardware faults or noisy sensors. We propose MOSAIC, an
algorithm for measuring the safety of deep reinforcement learning agents in
stochastic settings. Our approach is based on the iterative construction of a
formal abstraction of a controller's execution in an environment, and leverages
probabilistic model checking of Markov decision processes to produce
probabilistic guarantees on safe behaviour over a finite time horizon. It
produces bounds on the probability of safe operation of the controller for
different initial configurations and identifies regions where correct behaviour
can be guaranteed. We implement and evaluate our approach on agents trained for
several benchmark control problems
Online Robot Introspection via Wrench-based Action Grammars
Robotic failure is all too common in unstructured robot tasks. Despite
well-designed controllers, robots often fail due to unexpected events. How do
robots measure unexpected events? Many do not. Most robots are driven by the
sense-plan act paradigm, however more recently robots are undergoing a
sense-plan-act-verify paradigm. In this work, we present a principled
methodology to bootstrap online robot introspection for contact tasks. In
effect, we are trying to enable the robot to answer the question: what did I
do? Is my behavior as expected or not? To this end, we analyze noisy wrench
data and postulate that the latter inherently contains patterns that can be
effectively represented by a vocabulary. The vocabulary is generated by
segmenting and encoding the data. When the wrench information represents a
sequence of sub-tasks, we can think of the vocabulary forming a sentence (set
of words with grammar rules) for a given sub-task; allowing the latter to be
uniquely represented. The grammar, which can also include unexpected events,
was classified in offline and online scenarios as well as for simulated and
real robot experiments. Multiclass Support Vector Machines (SVMs) were used
offline, while online probabilistic SVMs were are used to give temporal
confidence to the introspection result. The contribution of our work is the
presentation of a generalizable online semantic scheme that enables a robot to
understand its high-level state whether nominal or abnormal. It is shown to
work in offline and online scenarios for a particularly challenging contact
task: snap assemblies. We perform the snap assembly in one-arm simulated and
real one-arm experiments and a simulated two-arm experiment. This verification
mechanism can be used by high-level planners or reasoning systems to enable
intelligent failure recovery or determine the next most optima manipulation
skill to be used.Comment: arXiv admin note: substantial text overlap with arXiv:1609.0494
Research on new techniques for the analysis of manual control systems Progress report, 15 Jun. 1969 - 15 Jun. 1970
Applying statistical decision theory to manual adaptive control system
Report from GI-Dagstuhl Seminar 16394: Software Performance Engineering in the DevOps World
This report documents the program and the outcomes of GI-Dagstuhl Seminar
16394 "Software Performance Engineering in the DevOps World".
The seminar addressed the problem of performance-aware DevOps. Both, DevOps
and performance engineering have been growing trends over the past one to two
years, in no small part due to the rise in importance of identifying
performance anomalies in the operations (Ops) of cloud and big data systems and
feeding these back to the development (Dev). However, so far, the research
community has treated software engineering, performance engineering, and cloud
computing mostly as individual research areas. We aimed to identify
cross-community collaboration, and to set the path for long-lasting
collaborations towards performance-aware DevOps.
The main goal of the seminar was to bring together young researchers (PhD
students in a later stage of their PhD, as well as PostDocs or Junior
Professors) in the areas of (i) software engineering, (ii) performance
engineering, and (iii) cloud computing and big data to present their current
research projects, to exchange experience and expertise, to discuss research
challenges, and to develop ideas for future collaborations
AUTOMATED TESTING OF SIMULINK/STATEFLOW MODELS IN THE AUTOMOTIVE DOMAIN
Context. Simulink/Stateflow is an advanced system modeling platform which is prevalently used in the Cyber Physical Systems domain, e.g., automotive industry, to implement software con- trollers. Testing Simulink models is complex and poses several challenges to research and prac- tice. Simulink models often have mixed discrete-continuous behaviors and their correct behav- ior crucially depends on time. Inputs and outputs of Simulink models are signals, i.e., values evolving over time, rather than discrete values. Further, Simulink models are required to operate satisfactory for a large variety of hardware configurations. Finally, developing test oracles for Simulink models is challenging, particularly for requirements capturing their continuous aspects. In this dissertation, we focus on testing mixed discrete-continuous aspects of Simulink models, an important, yet not well-studied, problem. The existing Simulink testing techniques are more amenable to testing and verification of logical and state-based properties. Further, they are mostly incompatible with Simulink models containing time-continuos blocks, and floating point and non- linear computations. In addition, they often rely on the presence of formal specifications, which are expensive and rare in practice, to automate test oracles.
Approach. In this dissertation, we propose a set of approaches based on meta-heuristic search and machine learning techniques to automate testing of software controllers implemented in Simulink. The work presented in this dissertation is motived by Simulink testing needs at Delphi Automotive Systems, a world leading part supplier to the automotive industry. To address the above-mentioned challenges, we rely on discrete-continuous output signals of Simulink models and provide output- based black-box test generation techniques to produce test cases with high fault-revealing ability. Our algorithms are black-box, hence, compatible with Simulink/Stateflow models in their en- tirety. Further, we do not rely on the presence of formal specifications to automate test oracles. Specifically, we propose two sets of test generation algorithms for closed-loop and open-loop con- trollers implemented in Simulink: (1) For closed-loop controllers, test oracles can be formalized and automated relying on the feedback received from the controlled system. We characterize the desired behavior of closed-loop controllers in a set of common requirements, and then use search to identify the worst-case test scenarios of the controller with respect to each requirement. (2) For open-loop controllers, we cannot automate test oracles since the feedback is not available, and test oracles are manual. Hence, we focus on providing test generation algorithms that develop small effective test suites with high fault revealing ability. We further provide a test case prioriti- zation algorithm to rank the generated test cases based on their fault revealing ability and lower the manual oracle cost.
Our test generation and prioritization algorithms are evaluated with several industrial and publicly available Simulink models. Specifically, we showed that fault revealing ability of our our approach outperforms that of Simulink Design Verifier (SLDV), the only test generation toolbox of Simulink and a well-known commercial Simulink testing tool. In addition, using our approach, we were able to detect several real faults in Simulink models from our industry partner, Delphi, which had not been previously found by manual testing based on domain expertise and existing Simulink testing tools.
Contributions. The main research contributions in this dissertation are:
1. An automated approach for testing closed-loop controllers that characterize the desired be- havior of such controllers in a set of common requirements, and combines random explo-
ration and search to effectively identify the worst-case test scenarios of the controller with
respect to each requirement.
2. An automated approach for testing highly configurable closed-loop controllers by account-
ing for all their feasible configurations and providing strategies to scale the search to large
multi-dimensional spaces relying on dimensionality reduction and surrogate modelling
3. A black-box output-based test generation algorithm for open-loop Simulink models which uses search to maximize the likelihood of presence of specific failure patterns (i.e., anti-
patterns) in Simulink output signals.
4. A black-box output-based test generation algorithm for open-loop Simulink models that
maximizes output diversity to develop small test suites with diverse output signal shapes
and, hence, high fault revealing ability.
5. A test case prioritization algorithm which relies on output diversity of the generated test
suites, in addition to the dynamic structural coverage achieved by individual tests, to rank
test cases and help engineers identify faults faster by inspecting a few test cases.
6. Two test generation tools, namely CoCoTest and SimCoTest, that respectively implement
our test generation approaches for closed-loop and open-loop controllers
Identifying how automation can lose its intended benefit along the development process : a research plan
Doctoral Consortium Presentation © The Authors 2009Automation is usually considered to improve performance in virtually any domain. However it can fail to deliver the target benefit as intended by those managers and designers advocating the introduction of the tool. In safety critical domains this problem is of significance not only because the unexpected effects of automation might prevent its widespread usage but also because they might turn out to be a contributor to incident and accidents. Research on failures of automation to deliver the intended benefit has focused mainly on human automation interaction. This paper presents a PhD research plan that aims at characterizing decisions for those involved in development process of automation for safety critical domains, taken under productive pressure, to identify where and when the initial intention the automation is supposed to deliver can be lost along the development process. We tentatively call such decisions as drift and the final objective is to develop principles that will allow to identify and compensate for possible sources of drift in the development of new automation. The research is based on case studies and is currently entering Year 2
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