All posts by Robotics News - Robot News, Robotics, Robots, Robotics Sciences

The Google-owned Waymo robotaxi service on Tuesday became available to anyone in San Francisco interested in hailing a driverless ride.

A city council in South Korea said Wednesday their first administrative officer robot was defunct after throwing itself down some stairs, with local media mourning the country's first robot suicide.

Researchers have found a way to bind engineered skin tissue to the complex forms of humanoid robots. This brings with it potential benefits to robotic platforms such as increased mobility, self-healing abilities, embedded sensing capabilities and an increasingly lifelike appearance.

As sub-symbolic AI, like deep learning, continues to advance, its limitations in safety and reliability are becoming more apparent. Verification and stability are crucial in safety-critical domains such as humanoid robotics, which is rapidly evolving into a versatile tool for various applications. However, proving the correctness of AI-based self-learning algorithms is challenging due to their uncertain inferences and opaque decision-making processes.

Research on energy efficiency enhancement of a tortoise-inspired legged robot, by a research team led by Dongwon Yun, professor at the Department of Robotics and Mechatronics Engineering at DGIST, has been featured on the cover of Advanced Intelligent Systems.

Meet CARMEN, short for Cognitively Assistive Robot for Motivation and Neurorehabilitation—a small, tabletop robot designed to help people with mild cognitive impairment (MCI) learn skills to improve memory, attention, and executive functioning at home.

An elephant uses its trunk for eating, drinking water, communicating, exploring the environment, social behavior, and making and using tools. The trunk, which contains six muscle groups, is not only very strong—it can uproot a tree—but can be used with great precision. Elephants use a number of techniques to grasp objects, including suction, pinching with the two "fingers" at the tip of the trunk and wrapping the trunk around the object.

Robots that can navigate various terrains both rapidly and efficiently could be highly advantageous, as they could successfully complete complex missions in challenging environments. For instance, these robots could help to monitor complex natural environments, such as forests, or could search for survivors after natural disasters.

To successfully complete missions in dynamic and unstructured real-world environments, mobile robots should be able to adapt their actions in real-time to avoid collisions with nearby objects, people or animals.

Engineers at The University of Manchester have unlocked the secrets to designing a robot capable of jumping 120 meters—higher than any other jumping robot designed to date.

Eindhoven researchers have developed a soft robotic "hand" made from liquid crystals and graphene that could be used to design future surgical robots. The new work has just been published in the journal ACS Applied Materials & Interfaces.

Dr. Peter King, a manufacturing researcher at our Clayton site, came across a problem in the lab six years ago. He found that before the team could start any lab work, they had to spend a lot of time programming.

New research from the University of Massachusetts Amherst shows that programming robots to create their own teams and voluntarily wait for their teammates results in faster task completion, with the potential to improve manufacturing, agriculture and warehouse automation.

While roboticists have introduced increasingly sophisticated systems over the past decades, teaching these systems to successfully and reliably tackle new tasks has often proved challenging. Part of this training entails mapping high-dimensional data, such as images collected by on-board RGB cameras, to goal-oriented robotic actions.

The performance of artificial intelligence (AI) tools, including large computational models for natural language processing (NLP) and computer vision algorithms, has been rapidly improving over the past decades. One reason for this is that datasets to train these algorithms have exponentially grown, collecting hundreds of thousands of images and texts often collected from the internet.