69 research outputs found
Revision of Specification Automata under Quantitative Preferences
We study the problem of revising specifications with preferences for automata
based control synthesis problems. In this class of revision problems, the user
provides a numerical ranking of the desirability of the subgoals in their
specifications. When the specification cannot be satisfied on the system, then
our algorithms automatically revise the specification so that the least
desirable user goals are removed from the specification. We propose two
different versions of the revision problem with preferences. In the first
version, the algorithm returns an exact solution while in the second version
the algorithm is an approximation algorithm with non-constant approximation
ratio. Finally, we demonstrate the scalability of our algorithms and we
experimentally study the approximation ratio of the approximation algorithm on
random problem instances.Comment: 9 pages, 3 figures, 3 tables, in Proceedings of the IEEE Conference
on Robotics and Automation, May 201
ViSpec: A graphical tool for elicitation of MTL requirements
One of the main barriers preventing widespread use of formal methods is the
elicitation of formal specifications. Formal specifications facilitate the
testing and verification process for safety critical robotic systems. However,
handling the intricacies of formal languages is difficult and requires a high
level of expertise in formal logics that many system developers do not have. In
this work, we present a graphical tool designed for the development and
visualization of formal specifications by people that do not have training in
formal logic. The tool enables users to develop specifications using a
graphical formalism which is then automatically translated to Metric Temporal
Logic (MTL). In order to evaluate the effectiveness of our tool, we have also
designed and conducted a usability study with cohorts from the academic student
community and industry. Our results indicate that both groups were able to
define formal requirements with high levels of accuracy. Finally, we present
applications of our tool for defining specifications for operation of robotic
surgery and autonomous quadcopter safe operation.Comment: Technical report for the paper to be published in the 2015 IEEE/RSJ
International Conference on Intelligent Robots and Systems held in Hamburg,
Germany. Includes 10 pages and 19 figure
Extended LTLvis Motion Planning interface (Extended Technical Report)
This paper introduces an extended version of the Linear Temporal Logic (LTL)
graphical interface. It is a sketch based interface built on the Android
platform which makes the LTL control interface more straightforward and
friendly to nonexpert users. By predefining a set of areas of interest, this
interface can quickly and efficiently create plans that satisfy extended plan
goals in LTL. The interface can also allow users to customize the paths for
this plan by sketching a set of reference trajectories. Given the custom paths
by the user, the LTL specification and the environment, the interface generates
a plan balancing the customized paths and the LTL specifications. We also show
experimental results with the implemented interface.Comment: 8 pages, 15 figures, a technical report for the 2016 IEEE
International Conference on Systems, Man, and Cybernetics (SMC 2016
Deep Predictive Models for Collision Risk Assessment in Autonomous Driving
In this paper, we investigate a predictive approach for collision risk
assessment in autonomous and assisted driving. A deep predictive model is
trained to anticipate imminent accidents from traditional video streams. In
particular, the model learns to identify cues in RGB images that are predictive
of hazardous upcoming situations. In contrast to previous work, our approach
incorporates (a) temporal information during decision making, (b) multi-modal
information about the environment, as well as the proprioceptive state and
steering actions of the controlled vehicle, and (c) information about the
uncertainty inherent to the task. To this end, we discuss Deep Predictive
Models and present an implementation using a Bayesian Convolutional LSTM.
Experiments in a simple simulation environment show that the approach can learn
to predict impending accidents with reasonable accuracy, especially when
multiple cameras are used as input sources.Comment: 8 pages, 4 figure
- …