The evolution of SDLs is charts their evolution from bespoke systems to interoperable platforms, highlighting challenges in scalability, generalizability and data provenance, and outlining pathways towards networked, trustworthy infrastructures enabling collective scientific superintelligence.
Self-driving laboratories (SDLs) integrate automation, robotics, and artificial intelligence to autonomously execute scientific experiments. As SDLs evolve toward concurrent multi-task execution and support for heterogeneous experiments, experimental scheduling becomes a critical decision-making layer, determining the temporal order and execution timing of operations under strict scientific constraints, including operation precedence, station allocation, batch processing, experimental parameters, and critically, time synchronization between consecutive operations. For example, in inorganic synthesis, temporal deviations during nucleation can fundamentally alter material properties. While SDLs offer a promising route toward accelerated discovery, the absence of explicit scheduling for concurrent experiments leads to resource conflicts and uncontrolled interruptions. These deviations undermine the reproducibility and data quality essential for artificial intelligence modeling. Here, we present a multi-task scheduling algorithm that jointly accounts for scientific constraints. We integrated this algorithm into an SDL using a closed-loop communication architecture that enables tight coordination between scheduling and robotic experiment execution. The algorithm was validated through the concurrent multi-task synthesis of gold nanoparticles and metal–organic frameworks, distinct chemical reactions governed by nucleation and growth kinetics. By synchronizing robotic operations with experimental stations as well as chemical workflow, the algorithm preserves chemical fidelity and ensures consistent material quality across concurrent multi-task executions, unattainable with a conventional scheduling algorithm that does not adequately account for scientific constraints. This paradigm establishes a practical scheduling framework for concurrent multitasking in SDLs, ensuring the experimental consistency required to generate the high-fidelity datasets foundational to autonomous, AI-driven scientific discovery.
Junyi Zhou, Luyao Ge, Xiaobo Li et al.· Chemical Science· 0 citations
This review proposes a novel function-oriented taxonomy by categorizing architectures into perception-integrated and planning-integrated paradigms, and addresses critical challenges, particularly long-tail data scarcity and the deficiency in human-like decision-making.
Yunxing Chen, Guo Yu, Pengfei Ran et al.· Actuators· 0 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 safety challenges that are not systematically addressed in newly established laboratories. In this context, this work aims to raise awareness of robotic safety among chemists and biologists leading laboratory automation projects who may have limited access to industrial robotics expertise. To support a preliminary evaluation of existing or newly developed automated laboratory systems, we explain and demonstrate the use of a simple, structured safety assessment methodology based on ISO standards and tailored to laboratory environments. The framework combines established robotics safety standards with laboratory-specific considerations, including chemical hazards, human-robot interaction, and dynamic workflows. To facilitate its adoption by scientists, the methodology is illustrated through a case study conducted at the Swiss CAT+ West Hub autonomous laboratory, focusing on a multi-instrument analytical platform integrating collaborative robotic arms and mobile robotic systems. The proposed framework follows a six-step iterative process encompassing system definition, hazard identification, risk estimation, risk reduction, and validation. Its applicability was evaluated through the case study, in which sixteen hazards were identified, with robot-human collisions and chemical exposure representing the most critical risks. Experimental force and pressure measurements further demonstrated that widely used collaborative robots may exceed accepted safety thresholds under realistic operating conditions, particularly as a consequence of end-effector design and task-dependent motion characteristics. Risk mitigation strategies based on dynamic safety zoning, sensor-based human detection, and operational mode control were implemented to ensure compliance with safety requirements. The results highlight the need for systematic, context-specific safety assessments in autonomous laboratories and demonstrate that collaborative robots are not inherently safe without rigorous validation. This work provides a practical framework for the safe deployment of robotic systems in autonomous and digital laboratory environments.
This systematic review explores the intersection between Multi-Agent Systems and Digital Twins, with a particular focus on predictive maintenance applications in resource-constrained contexts and reveals that, despite significant progress, no existing system offers an integrated embedded-distributed hierarchical solution that simultaneously meets the requirements of Industry 5.0.
Through applied case studies in pharmaceutical discovery and financial systems, common design patterns that make agentic systems successful are analyzed, and practical mitigation strategies for failure modes are discussed, such as verification pipelines, fallback mechanisms, and human-in-the-loop supervision.
Grace Hui Yang, P. Venkit, Hooman Sedghamiz et al.· Proceedings of the 32nd ACM...· 0 citations
Agentic AI-for-Science can automate ideation, planning, and analysis, but final validation still depends on real experiments. A self-driving lab (SDL) can execute those experiments, yet the loop still has bottlenecks: the agent may spend too many rounds on low-value experiments, or each round may require a high-cost experiment. We target these two physical bottlenecks with one agent. First, a prior-aware agentic DOE loop uses domain knowledge and past results to propose feasible and informative next experiments, reducing trials-to-target. Second, a cost-aware surrogate agent predicts high-cost, high-resolution measurements from low-cost, low-resolution measurements. It chooses between a high- and a low-cost measurement based on the predicted uncertainty. We examine these directions in the biology and materials domains, respectively. Together, under a single agent, these components aim to accelerate the SDL loop by reducing both the number of loops and the cost per experiment.
