An Empirical Study of Interaction Bugs in ROS-based Software

• URL: http://arxiv.org/abs/2507.10235v1
• Date: Mon, 14 Jul 2025 12:56:24 GMT
• Title: An Empirical Study of Interaction Bugs in ROS-based Software
• Authors: Zhixiang Chen, Zhuangbin Chen, Xingjie Cai, Wei Li, Zibin Zheng,
• Abstract summary: This work presents an empirical study of interaction bugs (iBugs) within robotic systems built using the Robot Operating System (ROS)<n>The identified iBugs are categorized into three major types: intra-system iBugs, hardware iBugs, and environmental iBugs.<n>Several findingsa are derived that shed light on the nature of iBugs and suggest directions for improving their prevention and detection.
• Score: 27.942783950657166
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Modern robotic systems integrate multiple independent software and hardware components, each responsible for distinct functionalities such as perception, decision-making, and execution. These components interact extensively to accomplish complex end-to-end tasks. As a result, the overall system reliability depends not only on the correctness of individual components, but also on the correctness of their interactions. Failures often manifest at the boundaries between components, yet interaction-related reliability issues in robotics--referred to here as interaction bugs (iBugs)--remain underexplored. This work presents an empirical study of iBugs within robotic systems built using the Robot Operating System (ROS), a widely adopted open-source robotics framework. A total of 121 iBugs were analyzed across ten actively maintained and representative ROS projects. The identified iBugs are categorized into three major types: intra-system iBugs, hardware iBugs, and environmental iBugs, covering a broad range of interaction scenarios in robotics. The analysis includes an examination of root causes, fixing strategies, and the impact of these bugs. Several findingsa are derived that shed light on the nature of iBugs and suggest directions for improving their prevention and detection. These insights aim to inform the design of more robust and safer robotic systems.

Related papers

• From MAS to MARS: Coordination Failures and Reasoning Trade-offs in Hierarchical Multi-Agent Robotic Systems within a Healthcare Scenario [3.5262044630932254]
  Multi-agent robotic systems (MARS) build upon multi-agent systems by integrating physical and task-related constraints.<n>Despite the availability of advanced multi-agent frameworks, their real-world deployment on robots remains limited.
  arXiv  Detail & Related papers  (2025-08-06T17:54:10Z)
• Implementing a Robot Intrusion Prevention System (RIPS) for ROS 2 [0.4613900711472571]
  We have designed and implemented RIPS, an intrusion prevention system tailored for robotic applications based on ROS 2.<n>This manuscript provides a comprehensive exposition of the issue, the security aspects of ROS 2 applications, and the key points of the threat model we created for our robotic environment.
  arXiv  Detail & Related papers  (2024-12-26T16:25:34Z)
• HEIGHT: Heterogeneous Interaction Graph Transformer for Robot Navigation in Crowded and Constrained Environments [13.574462966591199]
  We study the problem of robot navigation in dense and interactive crowds with environmental constraints such as corridors and furniture.<n>Previous methods fail to consider all types of interactions among agents and obstacles, leading to unsafe and inefficient robot paths.<n>We propose a structured framework to learn robot navigation policies with reinforcement learning.
  arXiv  Detail & Related papers  (2024-11-19T00:56:35Z)
• Algorithmic Scenario Generation as Quality Diversity Optimization [8.010900084313414]
  The increasing complexity of robots and autonomous agents that interact with people highlights the critical need for approaches that systematically test them before deployment. This review paper describes the insights that we have gained from working on each component of the framework, and shows how integrating these components leads to the discovery of a diverse range of realistic and challenging scenarios.
  arXiv  Detail & Related papers  (2024-09-07T05:20:41Z)
• Commonsense Reasoning for Legged Robot Adaptation with Vision-Language Models [81.55156507635286]
  Legged robots are physically capable of navigating a diverse variety of environments and overcoming a wide range of obstructions. Current learning methods often struggle with generalization to the long tail of unexpected situations without heavy human supervision. We propose a system, VLM-Predictive Control (VLM-PC), combining two key components that we find to be crucial for eliciting on-the-fly, adaptive behavior selection.
  arXiv  Detail & Related papers  (2024-07-02T21:00:30Z)
• ROS-LLM: A ROS framework for embodied AI with task feedback and structured reasoning [74.58666091522198]
  We present a framework for intuitive robot programming by non-experts. We leverage natural language prompts and contextual information from the Robot Operating System (ROS) Our system integrates large language models (LLMs), enabling non-experts to articulate task requirements to the system through a chat interface.
  arXiv  Detail & Related papers  (2024-06-28T08:28:38Z)
• ROBUST: 221 Bugs in the Robot Operating System [0.256557617522405]
  We systematically curated a dataset of 221 bugs across 7 popular and diverse software systems. We produce historically accurate recreations of each of the 221 defective software versions in the form of Docker images. We use a grounded theory approach to examine and categorize their corresponding faults, failures, and fixes.
  arXiv  Detail & Related papers  (2024-04-04T17:49:38Z)
• RoboScript: Code Generation for Free-Form Manipulation Tasks across Real and Simulation [77.41969287400977]
  This paper presents textbfRobotScript, a platform for a deployable robot manipulation pipeline powered by code generation. We also present a benchmark for a code generation benchmark for robot manipulation tasks in free-form natural language. We demonstrate the adaptability of our code generation framework across multiple robot embodiments, including the Franka and UR5 robot arms.
  arXiv  Detail & Related papers  (2024-02-22T15:12:00Z)
• MMRNet: Improving Reliability for Multimodal Object Detection and Segmentation for Bin Picking via Multimodal Redundancy [68.7563053122698]
  We propose a reliable object detection and segmentation system with MultiModal Redundancy (MMRNet) This is the first system that introduces the concept of multimodal redundancy to address sensor failure issues during deployment. We present a new label-free multi-modal consistency (MC) score that utilizes the output from all modalities to measure the overall system output reliability and uncertainty.
  arXiv  Detail & Related papers  (2022-10-19T19:15:07Z)
• Cognitive architecture aided by working-memory for self-supervised multi-modal humans recognition [54.749127627191655]
  The ability to recognize human partners is an important social skill to build personalized and long-term human-robot interactions. Deep learning networks have achieved state-of-the-art results and demonstrated to be suitable tools to address such a task. One solution is to make robots learn from their first-hand sensory data with self-supervision.
  arXiv  Detail & Related papers  (2021-03-16T13:50:24Z)
• Integrated Benchmarking and Design for Reproducible and Accessible Evaluation of Robotic Agents [61.36681529571202]
  We describe a new concept for reproducible robotics research that integrates development and benchmarking. One of the central components of this setup is the Duckietown Autolab, a standardized setup that is itself relatively low-cost and reproducible. We validate the system by analyzing the repeatability of experiments conducted using the infrastructure and show that there is low variance across different robot hardware and across different remote labs.
  arXiv  Detail & Related papers  (2020-09-09T15:31:29Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.