The field of scientific laboratories is moving towards increased automation and use of advanced sensing technologies. Researchers are developing innovative methods for high-throughput characterization of materials, such as viscometry and chronoamperometry, which enable faster and more accurate measurements. Furthermore, the use of machine learning and computer vision is becoming more prevalent, allowing for the analysis of complex data and the development of more sophisticated automation systems. Noteworthy papers include:

• The introduction of a computer vision viscometer that can measure viscosity across a wide range of values, which has the potential to revolutionize the field of materials science.
• The development of an olfactory inertial odometry framework, which enables robots to navigate by scent, and has many potential applications in areas such as agriculture and food quality control.
• The creation of an open-source capping machine suitable for confined spaces, which can be easily integrated into automated workflows and has the potential to improve the efficiency of laboratory operations.
• The introduction of FLIP, a flowability-informed powder weighing framework, which can enhance robotic policy learning for granular material handling and has many potential applications in areas such as pharmaceuticals and chemicals.