In the young discipline of robotics-inspired biology, robots replace experimental animals, allowing researchers to learn about animals under a wider range of conditions than exist in nature or the laboratory. What is the secret behind the steady but oh-so-elegant way in which cats move? That’s the subject of a study in Frontiers in Neurorobotics by scientists from Osaka University, who built a novel, 47cm-long and 7.6kg-heavy robotic cat.

For the first time an autonomously flying quadrotor has outperformed two human pilots in a drone race. The success is based on a novel algorithm that was developed by researchers of the University of Zurich. It calculates time-optimal trajectories that fully consider the drones’ limitations.

This is a hard question to answer because many people (or their companies) specialize in one tool or another. Some simulators are better at one aspect of robotics than at others. When I’m asked to recommend the best simulation tool for robotics I have to find an expert and hope that they are current and across a wide range of simulation tools in order to give me the best advice, which was why I took particular note of the recent review paper from Australia’s CSIRO, “A Review of Physics Simulators for Robotics Applications”.

A team of researchers from the Wyss Institute for Biologically Inspired Engineering at Harvard University and the Massachusetts Institute of Technology has found a way to embed synthetic biology reactions into fabrics, creating wearable biosensors that can be customized to detect pathogens and toxins and alert the wearer.

The recent outbreak of the severe acute respiratory syndrome, caused by COVID-19 has spread globally in an unprecedented way. Robotics is a promising part of the solution to help control and mitigate its effects. This work presents the design and implementation of several perception questionnaires to assess healthcare providers’ level of acceptance and awareness of robotics for COVID-19 control in clinical scenarios.

Over the years, robots have gotten quite good at identifying objects — as long as they’re out in the open. MIT researchers have now designed a sharp-tipped robot finger equipped with tactile sensing to meet the challenge of identifying buried objects.

Engineers at EPFL have developed a predictive control model that allows swarms of drones to fly in cluttered environments quickly and safely. It works by enabling individual drones to predict their own behavior and that of their neighbors in the swarm.

In recent years, robots have gained artificial vision, touch, and even smell. “Researchers have been giving robots human-like perception,” says MIT Associate Professor Fadel Adib. In a new paper, Adib’s team is pushing the technology a step further. “We’re trying to give robots superhuman perception,” he says.

Mapping is an essential task in many robotics applications. To build a map, it is frequently assumed that the positions of the robots are a priori unknown and need to be estimated during operation. Multi-robot SLAM is a research direction that addresses the collective exploration and mapping of unknown environments by multi-robot systems. Yet, most results so far have been achieved for small groups of robots. Multi-robot SLAM is still a growing field, and a number of research directions are yet to be explored. Among them, swarm SLAM is an alternative, promising approach that takes advantage of the characteristics of robot swarms.

From swallowing pills to injecting insulin, patients frequently administer their own medication. But they don’t always get it right. Improper adherence to doctors’ orders is commonplace, accounting for thousands of deaths and billions of dollars in medical costs annually. MIT researchers have developed a system to reduce those numbers for some types of medications.

Nearly all real-world applications of reinforcement learning involve some degree of shift between the training environment and the testing environment. However, prior work has observed that even small shifts in the environment cause most RL algorithms to perform markedly worse. As we aim to scale reinforcement learning algorithms and apply them in the real world, it is increasingly important to learn policies that are robust to changes in the environment.

The ability for humans to generalize their knowledge and experiences to new situations is remarkable, yet poorly understood. For example, imagine a human driver that has only ever driven around their city in clear weather. Even though they never encountered true diversity in driving conditions, they have acquired the fundamental skill of driving, and can adapt reasonably fast to driving in neighboring cities, in rainy or windy weather, or even driving a different car, without much practice nor additional driver’s lessons. While humans excel at adaptation, building intelligent systems with common-sense knowledge and the ability to quickly adapt to new situations is a long-standing problem in artificial intelligence.

Most of the ocean is unknown. Yet we know that the most challenging environments on the planet reside in it. Understanding the ocean in its totality is a key component for the sustainable development of human activities and for the mitigation of climate change, as proclaimed by the United Nations. We are glad to share our perspective about the role of soft robots in ocean exploration and offshore operations at the outset of the ocean decade (2021-2030).

Minimally invasive surgeries in which surgeons gain access to internal tissues through natural orifices or small external excisions are common practice in medicine. They are performed for problems as diverse as delivering stents through catheters, treating abdominal complications, and performing transnasal operations at the skull base in patients with neurological conditions.

Schools of fish exhibit complex, synchronized behaviors that help them find food, migrate, and evade predators. No one fish or sub-group of fish coordinates these movements, nor do fish communicate with each other about what to do next. Rather, these collective behaviors emerge from so-called implicit coordination — individual fish making decisions based on what they see their neighbors doing.