This paper presents innovative designs for 3D-printed tumbling microrobots, specifically engineered for targeted in vivo drug delivery applications. The microrobot designs, created using stereolithography 3D printing technologies, incorporate permanent micro-magnets to enable actuation via a rotating magnetic field actuator system. The experimental framework encompasses a series of locomotion characterization tests to evaluate microrobot performance under various conditions. Testing variables include variations in microrobot geometries, actuation frequencies, and environmental conditions, such as dry and wet environments, and temperature changes. The paper outlines designs for three drug loading methods, along with comprehensive assessments thermal drug release using a focused ultrasound system, as well as biocompatibility tests. Animal model testing involves tissue phantoms and in vivo rat models, ensuring a thorough evaluation of the microrobots' performance and compatibility. The results highlight the robustness and adaptability of the proposed microrobot designs, showcasing the potential for efficient and targeted in vivo drug delivery. This novel approach addresses current limitations in existing tumbling microrobot designs and paves the way for advancements in targeted drug delivery within the large intestine.

Recently, large language models(LLMs) have played an increasingly important role in solving a wide range of NLP tasks, leveraging their capabilities of natural language understanding and generating. Integration with external tools further enhances LLMs' effectiveness, providing more precise, timely, and specialized responses. However, LLMs still encounter difficulties with non-executable actions and improper actions, which are primarily attributed to incorrect parameters. The process of generating parameters by LLMs is confined to the tool level, employing the coarse-grained strategy without considering the different difficulties of various tools. To address this issue, we propose TUMS, a novel framework designed to enhance the tool-use capabilities of LLMs by transforming tool-level processing into parameter-level processing. Specifically, our framework consists of four key components: (1) an intent recognizer that identifies the user's intent to help LLMs better understand the task; (2) a task decomposer that breaks down complex tasks into simpler subtasks, each involving a tool call; (3) a subtask processor equipped with multi-structure handlers to generate accurate parameters; and (4) an executor. Our empirical studies have evidenced the effectiveness and efficiency of the TUMS framework with an average of 19.6\% and 50.6\% improvement separately on easy and hard benchmarks of ToolQA, meanwhile, we demonstrated the key contribution of each part with ablation experiments, offering more insights and stimulating future research on Tool-augmented LLMs.

Meningeal lymphatic vessels (MLVs) are responsible for the drainage of waste products from the human brain. An impairment in their functionality has been associated with aging as well as brain disorders like multiple sclerosis and Alzheimer's disease. However, MLVs have only recently been described for the first time in magnetic resonance imaging (MRI), and their ramified structure renders manual segmentation particularly difficult. Further, as there is no consistent notion of their appearance, human-annotated MLV structures contain a high inter-rater variability that most automatic segmentation methods cannot take into account. In this work, we propose a new rater-aware training scheme for the popular nnU-Net model, and we explore rater-based ensembling strategies for accurate and consistent segmentation of MLVs. This enables us to boost nnU-Net's performance while obtaining explicit predictions in different annotation styles and a rater-based uncertainty estimation. Our final model, MLV$^2$-Net, achieves a Dice similarity coefficient of 0.806 with respect to the human reference standard. The model further matches the human inter-rater reliability and replicates age-related associations with MLV volume.

Today, selecting an optimal robot, its base pose, and trajectory for a given task is currently mainly done by human expertise or trial and error. To evaluate automatic approaches to this combined optimization problem, we introduce a benchmark suite encompassing a unified format for robots, environments, and task descriptions. Our benchmark suite is especially useful for modular robots, where the multitude of robots that can be assembled creates a host of additional parameters to optimize. We include tasks such as machine tending and welding in completely synthetic environments and 3D scans of real-world machine shops. The benchmark suite defines these optimization problems and facilitates the comparison of solution algorithms. All benchmarks are accessible through cobra.cps.cit.tum.de, a platform to conveniently share, reference, and compare tasks, robot models, and solutions.

Removing litter from oceans and seas is a costly and time-consuming process. As part of a European cooperative project, a team at the Technical University of Munich (TUM) is developing a robotic system that uses machine learning methods to locate and collect waste under water. Our seas and oceans currently contain somewhere between 26 and 66 million tons of plastic waste, most of which is lying on the seafloor. This represents an enormous threat to marine plants and animals and to the ecological balance of the seas. But removing waste from the waters is a complex and expensive process.

Digital medicine is opening up entirely new possibilities. For example, it can detect tumors at an early stage. But the effectiveness of new AI algorithms depends on the quantity and quality of the data used to train them. To maximize the data pool, it is customary to share patient data between clinics by sending copies of databases to the clinics where the algorithm is being trained. For data protection purposes, the material usually undergoes anonymization and pseudonymization processes – a procedure that has also come in for criticism.

Sami Haddadin runs a'robot kindergarten' where intelligent machines learn from each other.Credit: Technical University of Munich "AI and robotics development pull us right into the heart of what it is to be human," says Sami Haddadin, founding director of the Munich School of Robotics and Machine Intelligence (MSRM) at TUM. "We're not looking to usher in an'age of automatons'. Rather, we hope to enable a smooth transition to an age of human- machine interaction." MSRM's research agenda covers the understanding of humans in order to develop intelligent machines that can, in turn, help humans. Haddadin gives an example: give a young child a key and, within around 20 tries, they can unlock a door. A child's intuitive ability to manipulate a tool is one aspect, but they also watch and learn from adults.

An interdisciplinary team at the Technical University of Munich (TUM) advocates the integration of ethics from the very beginning of the development process of new technologies. Alena Buyx, professor of ethics in medicine and health technologies, explains the embedded ethics approach. The discussions surrounding a greater emphasis on ethics in AI research have greatly intensified in recent years, to the point that one might speak of "ethics hype," and many committees in Germany and around the world such as the German Ethics Council or the EU Commission High-Level Expert Group on Artificial Intelligence have responded. They are all in agreement: More ethics are required in the development of AI-based health technologies. But how do things look in practice for engineers and designers?

Prof. Daniel Rückert has developed trailblazing computational techniques that generate highly informative images from CT and MRI scans, analyze them, and interpret them for improved medical diagnostics. TUM has now succeeded in recruiting this expert in AI-based medicine. The Alexander von Humboldt Foundation will support his research with one of the prestigious Humboldt Professorships, which comes with a 5 million euro endowment. With these awards, the Alexander von Humboldt Foundation is pursuing the goal of attracting leading international scientists to Germany on a long-term basis. In May, Federal Minister of Education and Research Anja Karliczek announced the goal of creating additional AI-related chairs.

A research team at TUM (Technical University of Munich) has recently developed biologically-inspired artificial skin for robots. The technology is crucial for the advancements of robots if they are ever to improve our everyday lives in a safely manner. Giving robots artificial skin (combined with sophisticated algorithms) would allow them to sense contact, proximity, temperature, and surroundings in great detail. This technology would enable robots to move around carefully with low risks of accidents - especially when humans are nearby. And this is what the team at TUM achieved.