Lifeboat Foundation

Unmanned aerial vehicles (UAVs), commonly known as drones, are now used to capture images and carry out a wide range of missions in outdoor environments. While there are now several UAV designs with different advantages and characteristics, most conventional aerial robots are underactuated, meaning that they have fewer independent actuators than their degrees of freedom (DoF).

Underactuated systems are often more cost-effective and can be controlled using simpler control strategies than overactuated systems (i.e., robots that have more independent actuators than their DoF). Nonetheless, they are often less reliable and not as capable of precisely controlling their position and orientation.

Researchers at Tecnalia’s Basque Research and Technology Alliance (BRTA) in Spain recently developed a new overactuated aerial robot that can independently control the position and orientation of its main body. This robot, introduced in a paper published in Robotics and Autonomous Systems, has four quadrotors that cooperatively carry its central body.

A team of engineers at Google’s DeepMind Project has demonstrated a robot capable of playing amateur-level table tennis (ping-pong). The team has published a paper on the arXiv preprint server describing how they developed the robot, how well it performed at different ability levels and how human players responded to playing with the robot.

Over the past several years, robot scientists have been combining advancements in robot design with artificial intelligence, resulting in the development of robots with ever increasing abilities. In this new effort, the research team has developed an AI-based ping-pong player with the highest performance level ever for a robot.

Continue reading “DeepMind develops a robot that can play amateur level ping-pong” »

Picture this: hundreds of ant-sized robots climb over rubble, under rocks and between debris to inspect the damage of a fallen building before human rescuers explore on-site.

Downscaling legged robots to the size of an insect enables access to small spaces that humans and large robots cannot reach. A swarm of small robots can even collaborate like their insect counterparts to haul objects and protect one another. Picotaur, a new robot from the labs of Sarah Bergbreiter and Aaron Johnson is the first of its size, able to run, turn, push loads and climb miniature stairs.

“This robot has legs that are driven by multiple actuators so it can achieve various locomotion capabilities,” said Sukjun Kim, a recent Ph.D. graduate advised by Bergbreiter. “With multiple gait patterns, it can walk like other hexapod robots, similar to how a cockroach moves, but it can also hop from the ground to overcome obstacles.”

Scientists hope to accelerate the development of human-level AI using a network of powerful supercomputers — with the first of these machines fully operational by 2025.

A trio of chemists at the University of Copenhagen has developed an AI application that can be used to figure out the phase of x-rays that crystals have diffracted as part of efforts to predict the structure of small molecules.

New approach creates skilled robots:

MIT researchers have developed a groundbreaking algorithm that enables robots to rapidly learn and master complex tasks.

In the search for less energy-hungry artificial intelligence, some scientists are exploring living computers.

By Jordan Kinard

Artificial intelligence systems, even those as sophisticated as ChatGPT, depend on the same silicon-based hardware that has been the bedrock of computing since the 1950s. But what if computers could be molded from living biological matter? Some researchers in academia and the commercial sector, wary of AI’s ballooning demands for data storage and energy, are focusing on a growing field known as biocomputing. This approach uses synthetic biology, such as miniature clusters of lab-grown cells called organoids, to create computer architecture. Biocomputing pioneers include Swiss company FinalSpark, which earlier this year debuted its “Neuroplatform”—a computer platform powered by human-brain organoids—that scientists can rent over the Internet for $500 a month.

More than one singularity.

The singularity could soon be upon us. The PESTLE framework, developed by this episode’s guest Daniel Hulme, expresses not one but six types of singularity that could occur: political, environmental, social, technological, legal and economic. ‪@JonKrohnLearns‬ and Daniel Hulme discuss how each of these singularities could bring good to the world, aligning with human interests and pushing forward progress. They also talk about neuromorphic computing, machine consciousness, and applying AI at work.

Continue reading “The Six Singularities (There’s Not Just One)” »

To address challenges of training spiking neural networks (SNNs) at scale, the authors propose a scalable, approximation-free training method for deep SNNs using time-to-first-spike coding. They demonstrate enhanced performance and energy efficiency for neuromorphic hardware.