LTLMoP: Experimenting with language, Temporal Logic and robot control

Finucane, Cameron; Jing, Gangyuan; Kress-Gazit, Hadas

doi:10.1109/iros.2010.5650371

“…This approach (along with [8]) builds on the work in [21,20], using the high-level control framework in [11]. Here a task specification is parsed into structured English constructed around a subset of Linear Temporal Logic (LTL) ( [10]).…”

Section: Related Workmentioning

confidence: 99%

“…The approach in this paper uses the Linear Temporal Logic Mission Planning (LTLMoP [11]) framework. LTLMoP is a Python toolkit that allows a user to control a simulated or physical robot from a task specification written in structured English or LTL.…”

Section: High-level Control -Ltlmopmentioning

confidence: 99%

See 1 more Smart Citation

High-level control of modular robots

Castro

¹

,

Koehler

²

,

Kress-Gazit

³

2011

2011 IEEE/RSJ International Conference on Intelligent Robots and Systems

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Reconfigurable modular robots can exhibit different specializations by rearranging the same set of parts comprising them. Actuating modular robots can be complicated because of the many degrees of freedom that scale exponentially with the size of the robot. Effectively controlling these robots directly relates to how well they can be used to complete meaningful tasks. This paper discusses an approach for creating provably correct controllers for modular robots from high-level tasks defined with structured English sentences. While this has been demonstrated with simple mobile robots, the problem was enriched by considering the uniqueness of reconfigurable modular robots. These requirements are expressed through traits in the high-level task specification that store information about the geometry and motion types of a robot.Given a high-level problem definition for a modular robot, the approach in this paper deals with generating all lower levels of control needed to solve it. Information about different robot characteristics is stored in a library, and two tools for populating this library have been developed. The first approach is a physics-based simulator and gait creator for manual generation of motion gaits. The second is a genetic algorithm framework that uses traits to evaluate performance under various metrics. Demonstration is done through simulation and with the CKBot hardware platform.

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“…The problem outlined in Section 2.3 extends the work in [21,20,11], where high-level control of a non-reconfigurable mobile robot is performed through the following:…”

Section: High-level Control -Ltlmopmentioning

confidence: 99%

High-level control of modular robots

Castro

¹

,

Koehler

²

,

Kress-Gazit

³

2011

2011 IEEE/RSJ International Conference on Intelligent Robots and Systems

View full text Add to dashboard Cite

Reconfigurable modular robots can exhibit different specializations by rearranging the same set of parts comprising them. Actuating modular robots can be complicated because of the many degrees of freedom that scale exponentially with the size of the robot. Effectively controlling these robots directly relates to how well they can be used to complete meaningful tasks. This paper discusses an approach for creating provably correct controllers for modular robots from high-level tasks defined with structured English sentences. While this has been demonstrated with simple mobile robots, the problem was enriched by considering the uniqueness of reconfigurable modular robots. These requirements are expressed through traits in the high-level task specification that store information about the geometry and motion types of a robot.Given a high-level problem definition for a modular robot, the approach in this paper deals with generating all lower levels of control needed to solve it. Information about different robot characteristics is stored in a library, and two tools for populating this library have been developed. The first approach is a physics-based simulator and gait creator for manual generation of motion gaits. The second is a genetic algorithm framework that uses traits to evaluate performance under various metrics. Demonstration is done through simulation and with the CKBot hardware platform.

show abstract

“…[2], [3], [9], [12], [13], [15], [25]) advocates for applying the approach to create correct-byconstruction controllers. In this context, the specification is usually expressed using temporal logic, and the synthesized controllers, which are finite-state machines, are guaranteed to cause the robot to fulfill its tasks.…”

Section: Introductionmentioning

confidence: 99%

Correct High-level Robot Behavior in Environments with Unexpected Events

Wong

¹

,

Ehlers

²

,

Kress-Gazit

³

2014

Robotics: Science and Systems X

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Abstract-Synthesis of correct-by-construction robot controllers from high-level specifications has the advantage of providing guaranteed robot behavior under different environments. Typically, when such controllers are synthesized, assumptions that the user makes about the behavior of the environment, if any, are incorporated into the resulting controller. In practice, however, the environment assumptions may be unknown to the user, thus preventing the application of synthesis. Even if environment assumptions are available, they may not hold during the robot's execution due to modeling errors or unforeseen anomalous operating conditions.In this paper, we address both of these problems. We present an approach for synthesizing controllers that include built-in recovery transitions, enabling the robot to make progress towards its goals in the event of environment assumption violation, whenever possible. Furthermore, we present a process for automatically augmenting a specification with environment assumptions that are computed from the robot's observations at runtime. We start with a set of candidate assumptions that is updated whenever violated at runtime.

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LTLMoP: Experimenting with language, Temporal Logic and robot control

Cited by 149 publications

References 15 publications

High-level control of modular robots

High-level control of modular robots

High-level control of modular robots

Correct High-level Robot Behavior in Environments with Unexpected Events

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