EzSkiROS: enhancing robot skill composition with embedded DSL for early error detection
When developing general-purpose robot software components, we often lack complete knowledge of the specific contexts in which they will be executed. This limits our ability to make predictions, including our ability to detect program bugs statically. Since running a robot is an expensive task, findi...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Robotics and AI |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/frobt.2024.1363443/full |
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| author | Momina Rizwan Christoph Reichenbach Ricardo Caldas Matthias Mayr Volker Krueger |
| author_facet | Momina Rizwan Christoph Reichenbach Ricardo Caldas Matthias Mayr Volker Krueger |
| author_sort | Momina Rizwan |
| collection | DOAJ |
| description | When developing general-purpose robot software components, we often lack complete knowledge of the specific contexts in which they will be executed. This limits our ability to make predictions, including our ability to detect program bugs statically. Since running a robot is an expensive task, finding errors at runtime can prolong the debugging loop or even cause safety hazards. This paper proposes an approach to help developers catch these errors as soon as we have some context (typically at pre-launch time) with minimal additional efforts. We use embedded domain-specific language (DSL) techniques to enforce early checks. We describe design patterns suitable for robot programming and show how to use these design patterns for DSL embedding in Python, using two case studies on an open-source robot skill platform SkiROS2, designed for the composition of robot skills. These two case studies help us understand how to use DSL embedding on two abstraction levels: the high-level skill description that focuses on what the robot can do and under what circumstances and the lower-level decision-making and execution flow of tasks. Using our DSL EzSkiROS, we show how our design patterns enable robotics software platforms to detect bugs in the high-level contracts between the robot’s capabilities and the robot’s understanding of the world. We also apply the same techniques to detect bugs in the lower-level implementation code, such as writing behavior trees (BTs), to control the robot’s behavior based on its capabilities. We perform consistency checks during the code deployment phase, significantly earlier than the typical runtime checks. This enhances the overall safety by identifying potential issues with the skill execution before they can impact robot behavior. An initial study with SkiROS2 developers shows that our DSL-based approach is useful for finding bugs early and thus improving the maintainability of the code. |
| format | Article |
| id | doaj-art-3d40201b6ed246db8c31d9cc953fe973 |
| institution | Kabale University |
| issn | 2296-9144 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Robotics and AI |
| spelling | doaj-art-3d40201b6ed246db8c31d9cc953fe9732025-01-03T05:10:27ZengFrontiers Media S.A.Frontiers in Robotics and AI2296-91442025-01-011110.3389/frobt.2024.13634431363443EzSkiROS: enhancing robot skill composition with embedded DSL for early error detectionMomina Rizwan0Christoph Reichenbach1Ricardo Caldas2Matthias Mayr3Volker Krueger4Department of Computer Science, Faculty of Engineering (LTH), Lund University, Lund, SwedenDepartment of Computer Science, Faculty of Engineering (LTH), Lund University, Lund, SwedenDepartment of Computer Science and Engineering, Chalmers University of Technology, Gothenburg, SwedenDepartment of Computer Science, Faculty of Engineering (LTH), Lund University, Lund, SwedenDepartment of Computer Science, Faculty of Engineering (LTH), Lund University, Lund, SwedenWhen developing general-purpose robot software components, we often lack complete knowledge of the specific contexts in which they will be executed. This limits our ability to make predictions, including our ability to detect program bugs statically. Since running a robot is an expensive task, finding errors at runtime can prolong the debugging loop or even cause safety hazards. This paper proposes an approach to help developers catch these errors as soon as we have some context (typically at pre-launch time) with minimal additional efforts. We use embedded domain-specific language (DSL) techniques to enforce early checks. We describe design patterns suitable for robot programming and show how to use these design patterns for DSL embedding in Python, using two case studies on an open-source robot skill platform SkiROS2, designed for the composition of robot skills. These two case studies help us understand how to use DSL embedding on two abstraction levels: the high-level skill description that focuses on what the robot can do and under what circumstances and the lower-level decision-making and execution flow of tasks. Using our DSL EzSkiROS, we show how our design patterns enable robotics software platforms to detect bugs in the high-level contracts between the robot’s capabilities and the robot’s understanding of the world. We also apply the same techniques to detect bugs in the lower-level implementation code, such as writing behavior trees (BTs), to control the robot’s behavior based on its capabilities. We perform consistency checks during the code deployment phase, significantly earlier than the typical runtime checks. This enhances the overall safety by identifying potential issues with the skill execution before they can impact robot behavior. An initial study with SkiROS2 developers shows that our DSL-based approach is useful for finding bugs early and thus improving the maintainability of the code.https://www.frontiersin.org/articles/10.3389/frobt.2024.1363443/fullembedded domain-specific languagesrobot skillsskill-based control platformsbehavior treesdomain-specific language design patterns |
| spellingShingle | Momina Rizwan Christoph Reichenbach Ricardo Caldas Matthias Mayr Volker Krueger EzSkiROS: enhancing robot skill composition with embedded DSL for early error detection Frontiers in Robotics and AI embedded domain-specific languages robot skills skill-based control platforms behavior trees domain-specific language design patterns |
| title | EzSkiROS: enhancing robot skill composition with embedded DSL for early error detection |
| title_full | EzSkiROS: enhancing robot skill composition with embedded DSL for early error detection |
| title_fullStr | EzSkiROS: enhancing robot skill composition with embedded DSL for early error detection |
| title_full_unstemmed | EzSkiROS: enhancing robot skill composition with embedded DSL for early error detection |
| title_short | EzSkiROS: enhancing robot skill composition with embedded DSL for early error detection |
| title_sort | ezskiros enhancing robot skill composition with embedded dsl for early error detection |
| topic | embedded domain-specific languages robot skills skill-based control platforms behavior trees domain-specific language design patterns |
| url | https://www.frontiersin.org/articles/10.3389/frobt.2024.1363443/full |
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