Laboratory automation is a key driver for higher efficiency and reproducibility of experiments and measurements in natural science laboratories. One process that is particularly susceptible to both manual errors in the physical handling of labware, faulty data analyses, and incomplete reporting is the quantitative Polymerase Chain Reaction (qPCR). It is a ubiquitous analysis method in biolaboratories to amplify and measure the amount of a specific DNA sequence in a sample. Our system, which we call the qPCRBot, addresses these issues through three key pillars: automating data analysis and handling processes, standardizing data management and system communication protocols, and utilizing a robotic manipulator for labware transport. To achieve this, we developed a SiLA 2-based client-server architecture for unified and standardized access to both the qPCR device and the robot. For the manipulator, we implemented a Cartesian motion generator to ensure proper labware transport. We transform all experiment data to a standardized, XML-based format and integrate a widely-used Laboratory Information Management System for its storage. These developments collectively enable streamlined qPCR measurements without human interaction, thus enhancing both efficiency and reproducibility.

One of the most important aspects of autonomous systems is safety. This includes ensuring safe human-robot and safe robot-environment interaction when autonomously performing complex tasks or in collaborative scenarios. Al-though several methods have been introduced to tackle this, most are unsuitable for real-time applications and require carefully handcrafted obstacle descriptions. In this work, we propose a method combining high-frequency and real-time self and environment collision avoidance of a robotic manipulator with low-frequency, multimodal, and high-resolution environmental perceptions accumulated in a digital twin system. Our method is based on geometric primitives, so-called primitive skeletons. These, in turn, are information-compressed and real-time compatible digital representations of the robot's body and environment, automatically generated from ultra-realistic virtual replicas of the real world provided by the digital twin. Our approach is a key enabler for closing the loop between environment perception and robot control by providing the millisecond real-time control stage with a current and accurate world description, empowering it to react to environmental changes. We evaluate our whole-body collision avoidance on a 9-DOFs robot system through five experiments, demonstrating the functionality and efficiency of our framework.

Strategies for safe human-robot interaction (HRI), such as the well-established Safe Motion Unit, provide a velocity scaling for biomechanically safe robot motion. In addition, psychologically-based safety approaches are required for trustworthy HRI. Such schemes can be very conservative and robot motion complying with such safety approaches should be time efficient within the robot motion planning. In this study, we improve the efficiency of a previously introduced approach for psychologically-based safety in HRI via a Model Predictive Control robot motion planner that simultaneously adjusts Cartesian path and speed to minimise the distance to the target pose as fast as possible. A subordinate real-time motion generator ensures human physical safety by integrating the Safe Motion Unit. Our motion planner is validated by two experiments. The simultaneous adjustment of path and velocity accomplishes highly time efficient robot motion, while considering the human physical and psychological safety. Compared to direct path velocity scaling approaches our planner enables 28 % faster motion execution.

Laboratory automation is a suitable solution to establish higher reproducibility with less manual work and thus higher quality standards in life sciences. To date, mobile robots are capable of performing autonomous pick-and-place tasks in the laboratory, and specialized pipetting machines can be used for sequenced liquid handling. However, the complex and creative process of developing new research protocols requires flexible robotic systems that can perform tasks such as pipetting in more versatile ways. In addition, the correct technique, according to ISO standards, has a great influence on precision and accuracy and therefore on reproducibility. This paper introduces our Intelligent Robotic Lab Assistants in the framework of our holistic, human-like, but standardized paradigm for collaborative lab automation, AI.Laboratory. Our system demonstrates mastery of pipetting following ISO 8655 as a force-sensitive robotic manipulation skill, which is a key component of our taxonomy of cell culture skills and the first steps toward true intelligent robotic laboratory assistants. This intelligent robotic pipetting skill is a versatile tool for general handling of µL-liquids, using only standard laboratory equipment that can be flexibly positioned in the robot's workspace. To demonstrate its pipetting performance, flexible handling of small volumes from 10 µL to 1000 µL was experimentally validated to the ISO 8655 standard, demonstrating superhuman performance that outperformed laymen, human experts, and other commercial and non-commercial robotic pipetting systems.