pith. sign in

arxiv: 2409.07080 · v1 · pith:GVJIMMS4new · submitted 2024-09-11 · 💻 cs.RO

Scenario Execution for Robotics: A generic, backend-agnostic library for running reproducible robotics experiments and tests

classification 💻 cs.RO
keywords roboticsexperimentsscenarioapproachdomainexecutionreproducibletesting
0
0 comments X
read the original abstract

Testing and evaluation of robotics systems is a difficult and oftentimes tedious task due to the systems' complexity and a lack of tools to conduct reproducible robotics experiments. Additionally, almost all available tools are either tailored towards a specific application domain, simulator or middleware. Particularly scenario-based testing, a common practice in the domain of automated driving, is not sufficiently covered in the robotics domain. In this paper, we propose a novel backend- and middleware-agnostic approach for conducting systematic, reproducible and automatable robotics experiments called Scenario Execution for Robotics. Our approach is implemented as a Python library built on top of the generic scenario description language OpenSCENARIO 2 and Behavior Trees and is made publicly available on GitHub. In extensive experiments, we demonstrate that our approach supports multiple simulators as backend and can be used as a standalone Python-library or as part of the ROS2 ecosystem. Furthermore, we demonstrate how our approach enables testing over ranges of varying values. Finally, we show how Scenario Execution for Robotics allows to move from simulation-based to real-world experiments with minimal adaptations to the scenario description file.

This paper has not been read by Pith yet.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Replicable Simulation-Based Robot Validation through Provenance

    cs.RO 2026-05 unverdicted novelty 4.0

    Augmenting a simulation-based robot testing framework with provenance tracking and FAIR-aligned metadata enables end-to-end reconstruction of validation evidence.