An Empirical Study on Embodied Artificial Intelligence Robot (EAIR) Software Bugs

• URL: http://arxiv.org/abs/2507.18267v1
• Date: Thu, 24 Jul 2025 10:11:45 GMT
• Title: An Empirical Study on Embodied Artificial Intelligence Robot (EAIR) Software Bugs
• Authors: Zeqin Liao, Zibin Zheng, Peifan Reng, Henglong Liang, Zixu Gao, Zhixiang Chen, Wei Li, Yuhong Nan,
• Abstract summary: We conducted the first systematic study of 885 EAIR system bugs collected from 80 EAIR system projects to investigate their symptoms, underlying causes, and module distribution.<n>Our analysis takes considerable effort, which classifies these bugs into 18 underlying causes, 15 distinct symptoms, and identifies 13 affected modules.
• Score: 24.870244451120318
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Embodied Artificial Intelligence Robots (EAIR) is an emerging and rapidly evolving technological domain. Ensuring their program correctness is fundamental to their successful deployment. However, a general and in-depth understanding of EAIR system bugs remains lacking, which hinders the development of practices and techniques to tackle EAIR system bugs. To bridge this gap, we conducted the first systematic study of 885 EAIR system bugs collected from 80 EAIR system projects to investigate their symptoms, underlying causes, and module distribution. Our analysis takes considerable effort, which classifies these bugs into 18 underlying causes, 15 distinct symptoms, and identifies 13 affected modules. It reveals several new interesting findings and implications which help shed light on future research on tackling or repairing EAIR system bugs. First, among the 15 identified symptoms, our findings highlight 8 symptoms specific to EAIR systems, which is characterized by severe functional failures and potential physical hazards. Second, within the 18 underlying causes, we define 8 EAIR-specific causes, the majority of which stem from the intricate issues of AI- agent reasoning and decision making. Finally, to facilitate precise and efficient bug prediction, detection, and repair, we constructed a mapping between underlying causes and the modules in which they most frequently occur, which enables researchers to focus diagnostic efforts on the modules most susceptible to specific bug types.

Related papers

• Cognitive Foundations for Reasoning and Their Manifestation in LLMs [63.12951576410617]
  Large language models (LLMs) solve complex problems yet fail on simpler variants, suggesting they achieve correct outputs through mechanisms fundamentally different from human reasoning.<n>We synthesize cognitive science research into a taxonomy of 28 cognitive elements spanning reasoning invariants, meta-cognitive controls, representations for organizing reasoning & knowledge, and transformation operations.<n>We develop test-time reasoning guidance that automatically scaffold successful structures, improving performance by up to 66.7% on complex problems.
  arXiv  Detail & Related papers  (2025-11-20T18:59:00Z)
• ATLAS: A High-Difficulty, Multidisciplinary Benchmark for Frontier Scientific Reasoning [118.46980291324148]
  ATLAS is a large-scale, high-difficulty, and cross-disciplinary evaluation suite composed of approximately 800 original problems.<n>Its key features include: High Originality and Contamination Resistance, with all questions newly created or substantially adapted to prevent test data leakage.<n>Preliminary results on leading models demonstrate ATLAS's effectiveness in differentiating their advanced scientific reasoning capabilities.
  arXiv  Detail & Related papers  (2025-11-18T11:13:06Z)
• BugPilot: Complex Bug Generation for Efficient Learning of SWE Skills [59.003563837981886]
  High quality bugs are key to training the next generation of language model based software engineering (SWE) agents.<n>We introduce a novel method for synthetic generation of difficult and diverse bugs.
  arXiv  Detail & Related papers  (2025-10-22T17:58:56Z)
• Demystifying deep search: a holistic evaluation with hint-free multi-hop questions and factorised metrics [89.1999907891494]
  We present WebDetective, a benchmark of hint-free multi-hop questions paired with a controlled Wikipedia sandbox.<n>Our evaluation of 25 state-of-the-art models reveals systematic weaknesses across all architectures.<n>We develop an agentic workflow, EvidenceLoop, that explicitly targets the challenges our benchmark identifies.
  arXiv  Detail & Related papers  (2025-10-01T07:59:03Z)
• Hide and Seek with LLMs: An Adversarial Game for Sneaky Error Generation and Self-Improving Diagnosis [51.88592148135258]
  We propose Hide and Seek Game (HSG), a dynamic adversarial framework for error generation and diagnosis.<n>HSG involves two adversarial roles: Sneaky, which "hides" by generating subtle, deceptive reasoning errors, and Diagnosis, which "seeks" to accurately detect them.<n> Experiments on several math reasoning tasks show that HSG significantly boosts error diagnosis, achieving 16.8%--31.4% higher accuracy than baselines like GPT-4o.
  arXiv  Detail & Related papers  (2025-08-05T12:45:21Z)
• BugScope: Learn to Find Bugs Like Human [9.05553442116139]
  BugScope emulates how human auditors learn new bug patterns from representative examples and apply that knowledge during code auditing.<n>Our evaluation on a dataset of 40 real-world bugs drawn from 21 widely-used open-source projects demonstrates that BugScope achieves 87.04% precision.<n>Further testing on large-scale open-source systems, including the Linux kernel, uncovered 141 previously unknown bugs.
  arXiv  Detail & Related papers  (2025-07-21T14:34:01Z)
• A Systematic Survey on Debugging Techniques for Machine Learning Systems [5.747738795689893]
  Machine learning (ML) software poses unique challenges compared to traditional software.<n>Various methods have been proposed for testing, diagnosing, and repairing ML systems.<n>However, the big picture informing important research directions that fulfill developers needs is yet to unfold.
  arXiv  Detail & Related papers  (2025-03-05T03:57:20Z)
• A Comprehensive Study of Bug-Fix Patterns in Autonomous Driving Systems [16.72158049599736]
  We present an empirical study that investigates bug-fix patterns in autonomous driving systems (ADSes)<n>We analyze the commit histories and bug reports of two major autonomous driving projects, Apollo and Autoware, from 1,331 bug fixes.<n>Our study reveals several dominant bug-fix patterns, including those related to path planning, data flow, and configuration management.
  arXiv  Detail & Related papers  (2025-02-04T02:13:05Z)
• Evaluation of OpenAI o1: Opportunities and Challenges of AGI [100.85218639544654]
  o1-preview demonstrated remarkable capabilities, often achieving human-level or superior performance.<n>The model excelled in tasks requiring intricate reasoning and knowledge integration across various fields.<n>Overall results indicate significant progress towards artificial general intelligence.
  arXiv  Detail & Related papers  (2024-09-27T06:57:00Z)
• Visual Agents as Fast and Slow Thinkers [88.1404921693082]
  We introduce FaST, which incorporates the Fast and Slow Thinking mechanism into visual agents.<n>FaST employs a switch adapter to dynamically select between System 1/2 modes.<n>It tackles uncertain and unseen objects by adjusting model confidence and integrating new contextual data.
  arXiv  Detail & Related papers  (2024-08-16T17:44:02Z)
• DISCOVERYWORLD: A Virtual Environment for Developing and Evaluating Automated Scientific Discovery Agents [49.74065769505137]
  We introduce DISCOVERYWORLD, the first virtual environment for developing and benchmarking an agent's ability to perform complete cycles of novel scientific discovery. It includes 120 different challenge tasks spanning eight topics each with three levels of difficulty and several parametric variations. We find that strong baseline agents, that perform well in prior published environments, struggle on most DISCOVERYWORLD tasks.
  arXiv  Detail & Related papers  (2024-06-10T20:08:44Z)
• Progressing from Anomaly Detection to Automated Log Labeling and Pioneering Root Cause Analysis [53.24804865821692]
  This study introduces a taxonomy for log anomalies and explores automated data labeling to mitigate labeling challenges. The study envisions a future where root cause analysis follows anomaly detection, unraveling the underlying triggers of anomalies.
  arXiv  Detail & Related papers  (2023-12-22T15:04:20Z)
• Causal Disentanglement Hidden Markov Model for Fault Diagnosis [55.90917958154425]
  We propose a Causal Disentanglement Hidden Markov model (CDHM) to learn the causality in the bearing fault mechanism. Specifically, we make full use of the time-series data and progressively disentangle the vibration signal into fault-relevant and fault-irrelevant factors. To expand the scope of the application, we adopt unsupervised domain adaptation to transfer the learned disentangled representations to other working environments.
  arXiv  Detail & Related papers  (2023-08-06T05:58:45Z)
• Understanding the Issues, Their Causes and Solutions in Microservices Systems: An Empirical Study [11.536360998310576]
  Technical Debt, Continuous Integration, Exception Handling, Service Execution and Communication are the most dominant issues in systems. We found 177 types of solutions that can be applied to fix the identified issues.
  arXiv  Detail & Related papers  (2023-02-03T18:08:03Z)
• IM-IAD: Industrial Image Anomaly Detection Benchmark in Manufacturing [88.35145788575348]
  Image anomaly detection (IAD) is an emerging and vital computer vision task in industrial manufacturing. The lack of a uniform IM benchmark is hindering the development and usage of IAD methods in real-world applications. We construct a comprehensive image anomaly detection benchmark (IM-IAD), which includes 19 algorithms on seven major datasets.
  arXiv  Detail & Related papers  (2023-01-31T01:24:45Z)
• Adversarial Patch Generation for Automated Program Repair [0.0]
  NEVERMORE is a novel learning-based mechanism inspired by the adversarial nature of bugs and fixes. NEVERMORE is built upon the Generative Adrial Networks architecture and trained on historical bug fixes to generate repairs that closely mimic human-produced fixes. Our empirical evaluation on 500 real-world bugs demonstrates the effectiveness of NEVERMORE in bug-fixing, generating repairs that match human fixes for 21.2% of the examined bugs.
  arXiv  Detail & Related papers  (2020-12-21T00:34:29Z)

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.