Jul 2026· International Conference on Artificial Intelligence Testing· pp. 211-221· 0 citations· 70 references
Abstract
Robots are being deployed for an increasingly diverse set of purposes, from industrial manufacturing to delivery, inspection, and surgical assistance, and the systems entering these roles are markedly more capable than earlier generations, utilizing learned perception, language-based planning, and multi-sensor input. This increase in the number of deployments is reflected in industry forecasts that report rapid, sustained growth in industrial robot installations and in the worldwide operational stock [18]. As robots take on broader and more complex missions in human-centered environments, their quality assurance and physical safety become increasingly relevant. However, despite this growth and advancements, few existing works offer a comprehensive review of robotic test automation with its conventional, AI-powered, and agentic landscape and overall trends. This paper addresses this gap by summarizing, classifying, and visualizing conventional and AI-based robot testing. Extending this, a reading of the commercial landscape suggests that conventional and AI methods act as complements rather than substitutes. Lastly, this work portrays many problems, challenges, and needs to aid in future research.
The review finds that multimodal fusion and semantic SLAM are overcoming perception bottlenecks, that deep learning and force/position hybrid control are balancing flexible adaptability with high-precision operation, and that deep reinforcement learning and large models are advancing intelligent process planning.
Zheng-Yang Chen· Advances in Engineering Inno...· 0 citations
This analysis shows that the modularity, flexibility, and reactivity of classical BTs are insufficient for adaptation needs involving runtime restructuring, reasoning under uncertainty, mission reinterpretation, learning, or integration with external knowledge and planning mechanisms.
This paper identifies several common challenges encountered during industry deployment, including insufficient stability in recognizing complex scenes, difficulty balancing computational power with recognition accuracy, significant gaps between simulation training and real-world conditions, and a lack of unified applic...
Zi-Chen Wang· Journal of Computer Science...· 0 citations
The quantified requirements of industrial robots enabled by EAI4I are analyzed and recent research progress is reviewed, covering core technologies for single- and multi-robot systems, dedicated hardware platforms, high-fidelity simulators, task-specific datasets, representative industrial application scenarios, and cr...
Hai-Bin Yu, Chunhe Song, Yin-Long Zhang et al.· National Science Review· 0 citations
Research and current innovations required more open-source tools to enhance the multidisciplinary kind of research growth. Thus, open-source tools are very fruitful specially in case of robotics and automation systems. Because of this any user can design and test virtually before wasting the time and money to implement...
Priyanka Mishra, Upkar Singh Kandhari, Anchana B S et al.· 2026 4th International Confe...· 2 citations
The emergence of autonomous laboratories is accelerating discovery in chemistry, drug discovery, materials science, and related fields by enabling high-throughput, data-driven experimentation. However, the integration of heterogeneous robotic systems, ranging from fixed manipulators to mobile platforms, introduces safe...
