Systematic Bug Analysis Reveals Key Issues in Embodied Artificial Intelligence Robots

Embodied Artificial Intelligence Robots (EAIR) represent a rapidly advancing field, yet ensuring the reliability of their software remains a critical challenge for widespread adoption. Zeqin Liao, Zibin Zheng, and Peifan Reng, all from Sun Yat-sen University ZhuHai, alongside colleagues, present the first systematic study of 885 software bugs collected from 80 EAIR projects. Their research addresses a significant gap in understanding these complex systems, identifying common symptoms, underlying causes, and the modules most prone to errors. The team’s analysis reveals that EAIR systems exhibit unique failure modes, including severe functional issues and potential physical hazards, and that many bugs stem from the complexities of artificial intelligence reasoning and decision-making processes. By mapping these underlying causes to specific modules, the researchers provide valuable insights that will focus future efforts on improving the prediction, detection, and repair of EAIR software.

Robotic Bugs, Real World Impacts, and Analysis

This research summarizes a study of bugs in Embodied AI Systems, specifically robots interacting with the physical world. The investigation highlights the increasing complexity of these systems and the potential for significant consequences when bugs occur, ranging from minor inconveniences to safety hazards. Recognizing a need for systematic understanding, researchers undertook a detailed investigation into the types of bugs arising in these complex machines. The study involved analyzing a substantial dataset of bugs reported in real-world robotic systems, categorizing them by root cause, affected component, and resulting symptom.</p

Researchers identified common patterns and contributing factors, evaluating the potential impact of each bug on system functionality and safety. The findings reveal key categories of bugs, including issues with perception, planning, control, communication, and hardware, with concurrency and configuration bugs also identified. The research highlights that many bugs are not simple coding errors but result from complex interactions between hardware, software, and the environment. A significant portion of bugs relate to physical-world interactions, issues that wouldn’t arise in purely software systems.</p

This work has important implications for improving bug detection and prevention, enhancing system reliability and safety, and developing more effective testing strategies. The findings also provide a foundation for automated bug detection tools and a deeper understanding of the challenges in developing reliable embodied AI systems. In essence, this paper provides a valuable empirical study of bugs in real-world robots, offering insights to improve their design, development, and testing. It moves beyond simply identifying bugs to understanding why they occur and how to prevent them, emphasizing the importance of considering the physical world and complex interactions when building these systems.</p

Embodied AI Bug Dataset Construction and Reproduction

Researchers employed a meticulous methodology to investigate bugs within embodied artificial intelligence robot systems, focusing on real-world deployments and popular projects. Recognizing the lack of comprehensive understanding, they assembled a substantial dataset of 80 projects sourced from industry, academia, and open-source platforms like GitHub. This selection process ensured relevance to embodied AI, public availability, support for real-world hardware, and project popularity. Constructing this dataset required significant manual effort, including reproducing projects and creating a reproducible platform using Docker containers.</p

Following dataset creation, the team collected bug reports from open-source repositories, primarily through the GitHub API, extracting data from issues, pull requests, and commits. This initial collection yielded a large volume of data, which underwent rigorous filtering and manual inspection. Researchers prioritized reports with explicit bug labels and developer feedback, discarding those lacking reproducibility or representing enhancements rather than genuine flaws. Each remaining bug report was subjected to two critical conditions: demonstrable reproducibility and the presence of adverse symptoms. This careful process narrowed the initial collection down to a focused dataset of 885 confirmed bugs, forming the foundation for a detailed investigation into their underlying causes, symptoms, and affected modules. The resulting database provides a valuable resource for future research aimed at improving the reliability and robustness of embodied AI systems.</p

AI Reasoning Drives Embodied Robot Errors

Researchers have undertaken the first systematic investigation into the causes of errors in embodied artificial intelligence robots, revealing key insights into building reliable and safe physical agents. This study analyzed data from 885 bug reports across 80 different EAIR projects, identifying patterns in how and why these robots malfunction. The findings demonstrate that EAIR systems exhibit unique failure modes compared to traditional robotics, stemming from the complexities of integrating artificial intelligence with physical embodiment. The research highlights that a significant proportion of EAIR bugs are directly linked to the reasoning and decision-making processes of the AI agents controlling the robots.</p

Eight specific underlying causes of errors were found to be unique to EAIR systems, often resulting in severe functional failures and potential physical hazards. These issues frequently arise within modules responsible for embodied interaction, perception, simulation, and control, the core components that allow robots to understand and interact with their environment. Failures in embodied perception, where robots interpret their surroundings, are more complex than in traditional robotics, requiring a richer understanding of object relationships and dynamic scenes. The study also identified 15 distinct symptoms of these bugs and mapped them to the 13 modules within a typical EAIR system.</p

This mapping is valuable, as it allows researchers to pinpoint the most likely source of errors based on observed symptoms. By understanding which modules are prone to specific bug types, diagnostic efforts can be focused, accelerating repair and improvement. Debugging EAIR systems is considerably more challenging than traditional robotics due to the interplay between software, AI algorithms, and physical hardware. Furthermore, the research demonstrates that EAIR systems require more sophisticated simulation capabilities than traditional robots. Accurately modeling the richness and dynamics of the real world, including features like cloth, fluids, and soft-body physics, is crucial for training and testing AI agents. The study emphasizes the need for simulations that extend beyond basic interactivity, allowing for fine-grained interactions and complex scenarios. These findings underscore the importance of investing in advanced simulation technologies to ensure the safety and reliability of EAIR systems as they become increasingly integrated into everyday life.</p

EAIR Bugs, Causes and System Impacts

This study presents the first systematic characterisation of bugs in Embodied Artificial Intelligence Robot software, analysing a total of 885 bugs from 80 real-world projects. The research identifies 18 underlying causes and 15 distinct symptoms of these bugs, alongside the 13 system components most frequently affected. Notably, the analysis reveals eight symptoms and eight underlying causes specific to EAIR systems, often stemming from complex issues in AI agent reasoning and decision-making processes.</p