This study explores the potential of automating robotic laboratory readiness assessment by integrating Large Language Models (LLMs) with robotic data acquisition. It investigates the capability of LLMs to detect equipment motion and operational status using visual and auditory information. Despite the challenges LLMs face in spatial analysis, this study also investigates LLM grounding methods to ensure accurate workspace assessment. By inspecting a robotic cooking setup with camera-equipped robotic arm, LLMs can detect the motion of custom equipment via color-coded marks, and identify the operational status of kitchen appliances from a single image without any physical augmentations. Additionally, device operation perceived through the emission of loud noises can be assessed by post-processing sound recordings and analyzing loudness and sound frequency metrics presented in a visual plot form. For simple spatial tasks like saucepan positioning, LLM provides accurate assessments when grounded with a single image, while complex workspace safety assessment task requires extensive knowledge of past experiences. By reviewing status of each checklist item, the LLM can decide whether experiment needs to be halted or requires human intervention, offering a set of troubleshooting steps. These findings demonstrate feasibility of the self-assessment approach for robotic laboratory systems, paving the way for future deployments.

Laboratory experiments in modern food labs are human-driven and tedious processes which can have limited throughput, reliability, repeatability or robustness. Through repeatable motions and precise control of process parameters, robotic automation can provide significant improvements to the existing experimental processes, and also improve manual assessment of the sensory data. By developing a robotic automation system which performs the make, measure, adjust and clean processes for a milk beverage made from water and powdered milk, we explore how variation in different process parameters impacts quality of the beverage in terms of the measured pH value. Using collected data we also identify optimal process parameters from robustness and time-cost standpoint. By comparing performance of the robotic system to a human we demonstrate varied performance in the pH adjustment process and 3x better precision in the pH probe cleaning. We identify that designed robotic system requires 45% more time to perform the experiment when compared to a human, yet provides significant advances in terms of repeatability and reproducibility. These findings demonstrate feasibility and benefits of the robotic automation in the food lab environments, thus paving the way for the broader implementation.

For many manipulation tasks in environments such as laboratory or a kitchen, the presence of two robot arms is important to enable collaborative tasks requiring two arms (e.g. lid removal or tool use) or to improve the efficiency of scheduling of tasks. Currently, the development of multi-arm manipulation solutions has largely focused on 6 degrees of freedom articulated robot arms. However, cartesian robots have many advantages, including their precision, reliability, efficiency, and simple path planning. By developing a cartesian platform such that the end effectors of two mirrored systems can interact freely without collisions in 5 degrees of freedom, we can leverage the advantages of cartesian robots (high precision, simple planning, and low-cost hardware) and show robot cooperation. We equip each robot with end-effectors with different skills to increase the range of tasks the robots can cooperatively complete. To exploit this robotic hardware, we have developed a task-allocation and path-planning algorithm that enables these two mirror robots to work together to solve tasks collaboratively, exploiting the different skills and workspace of the two robots. We show how this robot can be used for cooperative tasks in lab automation, including pick and place, unscrewing vial caps, liquid pouring, and weighing. These demonstrate the feasibility and capabilities of the proposed robotic system for cooperative automation using cartesian robots.

We present RESCURE, a security solution built on software, which retrofits Internet of Things (IoT) devices to secure ones. RESCURE exploits the entropy originating from the random variations of silicon (transistors) during manufacturing and generates a unique unforgeable root key and an identity per device. In this way, root key and identity are inseparable from the IoT hardware. To achieve lifetime reliability (reproducibility) and security (randomness) for root key and identity, we apply error correcting and randomness amplification algorithms to the signals derived from silicon. RESCURE supports certificates which are able to prove the device identity and authenticity. RESCURE supports multiple keys derivation (private keys or private/public key pairs) and End-to-End security. In this way an IoT device is able to communicate securely and independently with multiple actors (e.g., Service Providers). It supports secure storage so it is able to encrypt sensitive data such as application keys, sensitive data or software Intellectual Properties (IP). Finally, the entire device software is protected by secure boot and secure software update mechanisms allowing for malware-free software execution and renewable security and features. RESCURE has been prototyped on an ST32L4 device and its performance is presented across real use case scenarios covering the entire life cycle of the device. It is a low-cost solution for all the devices manufacturers that want to achieve high standard security without redesigning the hardware of their IoT product.