Laboratory automation is an expensive and complicated endeavor with limited inflexible options for small-scale labs. We develop a prototype system for tending to a bench-top centrifuge using computer vision methods for color detection and circular Hough Transforms to detect and localize centrifuge buckets. Initial results show that the prototype is capable of automating the usage of regular bench-top lab equipment.

A combined task-level reinforcement learning and motion planning framework is proposed in this paper to address a multi-class in-rack test tube rearrangement problem. At the task level, the framework uses reinforcement learning to infer a sequence of swap actions while ignoring robotic motion details. At the motion level, the framework accepts the swapping action sequences inferred by task-level agents and plans the detailed robotic pick-and-place motion. The task and motion-level planning form a closed loop with the help of a condition set maintained for each rack slot, which allows the framework to perform replanning and effectively find solutions in the presence of low-level failures. Particularly for reinforcement learning, the framework leverages a distributed deep Q-learning structure with the Dueling Double Deep Q Network (D3QN) to acquire near-optimal policies and uses an A${}^\star$-based post-processing technique to amplify the collected training data. The D3QN and distributed learning help increase training efficiency. The post-processing helps complete unfinished action sequences and remove redundancy, thus making the training data more effective. We carry out both simulations and real-world studies to understand the performance of the proposed framework. The results verify the performance of the RL and post-processing and show that the closed-loop combination improves robustness. The framework is ready to incorporate various sensory feedback. The real-world studies also demonstrated the incorporation.

Teleoperation of robotic systems for precise and delicate object grasping requires high-fidelity haptic feedback to obtain comprehensive real-time information about the grasp. In such cases, the most common approach is to use kinesthetic feedback. However, a single contact point information is insufficient to detect the dynamically changing shape of soft objects. This paper proposes a novel telemanipulation system that provides kinesthetic and cutaneous stimuli to the user's hand to achieve accurate liquid dispensing by dexterously manipulating the deformable object (i.e., pipette). The experimental results revealed that the proposed approach to provide the user with multimodal haptic feedback considerably improves the quality of dosing with a remote pipette. Compared with pure visual feedback, the relative dosing error decreased by 66\% and task execution time decreased by 18\% when users manipulated the deformable pipette with a multimodal haptic interface in combination with visual feedback. The proposed technology can be potentially implemented in delicate dosing procedures during the antibody tests for COVID-19, chemical experiments, operation with organic materials, and telesurgery.

A biological computer controlled by DNA offers a cheap and simple way to test the concentration of contaminants in drinking water. And experiments show that logical operations borrowed from computer science can be engineered into the DNA to make future biological computers far more powerful at detecting contaminants. Julius Lucks at Northwestern University in Illinois and his colleagues created a biosensor in 2020 that can detect contaminants in a single drop of water. It contains proteins that react to the presence of certain chemicals by producing fluorescent molecules. This easily spotted reaction acts as a warning that a water sample is polluted with these chemicals.

This might seem like a purely academic debate. Whatever we call it, surely what matters most about "AI" is the way it is already transforming what can seem like almost every industry on earth? Not to mention the potential it has to displace millions of workers in trades ranging from white to blue collar, from the back office to trucking? And yet, across the fields it is disrupting or supposed to disrupt, AI has fallen short of many of the promises made by some of its most vocal advocates--from the disappointment of IBM's Watson to the forever-moving target date for the arrival of fully self-driving vehicles. And--ask any branding or marketing expert--names, in particular, carry weight.

It wasn't planned, but Alzheimer's disease has become a recurrent theme in the career of physicist Serge Rombouts, Professor of Cognitive Neuroimaging at the LUMC and the University's Institute of Psychology. He received a PhD in 1999 for a technique using an MRI scanner to visualise the brain activity of Alzheimer's patients, and dementia has continued to crop up in many of his projects ever since. The ultimate aim: to use a brain scan to determine whether someone has dementia, in addition to the interviews and memory tests that doctors use to reach a diagnosis. 'The problem is that at an early stage Alzheimer's disease has all sorts of similarities with other forms of dementia,' says Rombouts. 'You want to distinguish between these at as early a stage as possible, and this diagnosis is also useful if you want to try out new treatments.

Tasks in the pharmaceutical, life sciences and biomedical industries have always been time-consuming and complex. With the advent of the Covid-19 pandemic, these undertakings will only grow in complexity. To ensure speed, accuracy and mitigate the infectivity stress among the humans, robots are called upon to meet the ever-increasing range of workflows in today's research and development laboratories. Laboratory automation, drug discovery and pharmaceutical manufacturing are emerging fields where the services of robots are leveraged for research and development. Robotic lab assistants help researchers and scientists focus on high-level tasks like the analysis of potential therapeutic compounds rather than mundanely mixing compounds to determine their curative characteristics.

Last Wednesday, researchers at Caltech announced that they created an artificial neural network from synthetic DNA that is able to recognize numbers coded in molecules. It's a novel implementation of a classic machine learning test that demonstrates how the very building blocks of life can be harnessed as a computer. This is pretty mind blowing, but what does it all mean? For starters, "artificial intelligence" here doesn't refer to the superhuman AI that is so beloved by Hollywood. Instead, it refers to machine learning, a narrow form of artificial intelligence that is best summarized as the art and science of pattern recognition.

The wire, which is threaded into a vein, attracts special magnetic nanoparticles engineered to glom onto tumor cells that may be roaming the bloodstream if you have a tumor somewhere in your body. With these tumor cells essentially magnetized, the wire can lure the cells out of the free-flowing bloodstream using the same force that holds family photos to your refrigerator. The technique, which has only been used in pigs so far, attracts from 10-80 times more tumor cells than current blood-based cancer-detection methods, making it a potent tool to catch the disease earlier. The technique could even help doctors evaluate a patient's response to particular cancer treatments: If the therapy is working, tumor-cell levels in the blood should rise as the cells die and break away from the tumor, and then fall as the tumor shrinks. For now, Sam Gambhir, MD, PhD, professor and chair of radiology and director of the Canary Center at Stanford for Cancer Early Detection, is focused on the wire as a cancer-detection method, but its reach could be much broader.

Caltech scientists have recently developed an AI made out of DNA that can tackle a classic machine learning problem by precisely recognizing written by hand numbers. The work is a critical advance in showing the ability to program AI into engineered biomolecular circuits. Lulu Qian, assistant professor of bioengineering at Caltech said, "Though scientists have only just begun to explore creating artificial intelligence in molecular machines, its potential is already undeniable. Similar to how electronic computers and smartphones have made humans more capable than a hundred years ago, artificial molecular machines could make all things made of molecules, perhaps including even paint and bandages, more capable and more responsive to the environment in the hundred years to come." Scientists' goal behind this study is to program intelligent behaviors (the ability to compute, make choices, and more) with artificial neural networks made out of DNA.