Lifeboat Foundation

In recent years, researchers worldwide have been trying to develop sensors that could replicate humans’ sense of touch in robots and enhance their manipulation skills. While some of these sensors achieved remarkable results, most existing solutions have small sensitive fields or can only gather images with low-resolutions.

A team of researchers at UC Berkeley recently developed a new multi-directional tactile sensor, called OmniTact, that overcomes some of the limitations of previously developed sensors. OmniTact, presented in a paper pre-published on arXiv and set to be presented at ICRA 2020, acts as an artificial fingertip that allows robots to sense the properties of objects it is holding or manipulating.

“Our lab recognized the need for a sensor for general robotic manipulation tasks with expanded capabilities than current tactile sensors,” Frederik Ebert, one of the researchers who carried out the study, told TechXplore. ‘“Existing tactile sensors are either flat, have small sensitive fields or only provide low-resolution signals. For example, the GelSight sensor provides high resolution (i.e., 400×400 pixel) images but is large and flat, providing sensitivity on only one side, while the OptoForce sensor is curved, but only provides force readings at a single point.”

Micro aerial vehicles (MAVs) could have numerous useful applications, for instance, assisting humans in completing warehouse inventories or search and rescue missions. While many companies worldwide have already started producing and using MAVs, some of these flying robots still have considerable limitations.

To work most effectively, MAVs should be supported by an efficient pose estimation system. This is a system or method that can calculate a drone’s position and attitude, which can then be used to control its flight, adjust its speed and aid its navigation while it is operating autonomously and when controlled remotely.

Researchers at Huazhong University of Science and Technology in China have recently developed a new system for the pose estimation of MAVs in indoor environments. Their new approach, outlined in a paper pre-published on arXIv and set to be published in IEEE Transactions on Industrial Electronics, leverages existing WiFi infrastructure to enable more effective navigation in small and agile drones.

A new reinforcement-learning algorithm has learned to optimize the placement of components on a computer chip to make it more efficient and less power-hungry.

3D Tetris: Chip placement, also known as chip floor planning, is a complex three-dimensional design problem. It requires the careful configuration of hundreds, sometimes thousands, of components across multiple layers in a constrained area. Traditionally, engineers will manually design configurations that minimize the amount of wire used between components as a proxy for efficiency. They then use electronic design automation software to simulate and verify their performance, which can take up to 30 hours for a single floor plan.

Time lag: Because of the time investment put into each chip design, chips are traditionally supposed to last between two and five years. But as machine-learning algorithms have rapidly advanced, the need for new chip architectures has also accelerated. In recent years, several algorithms for optimizing chip floor planning have sought to speed up the design process, but they’ve been limited in their ability to optimize across multiple goals, including the chip’s power draw, computational performance, and area.

Greatings earthlings o.o!

Last week a four-day-long immersive show featuring robot puppets took place on the streets of Toulouse, France.

Continue reading “Giant Minotaur and spider robots perform in the streets of France” »

Though advancements still need to be made, robots and A.I. could help us fight the coronavirus outbreak and save lives.

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Amazon CEO Jeff Bezos and the World Health Organization’s director-general are trading ideas on how to get the COVID-19 pandemic under control, using tools ranging from Amazon Web Services’ firepower in cloud computing and artificial intelligence to distribution channels for coronavirus test kits.

Bezos recapped today’s talk with Director-General Tedros Adhanom Ghebreyesus in an Instagram post, featuring a screengrab of Bezos’ videoconference view with the billionaire’s own visage in the upper right corner of the frame:

I would love to see this on a long handle to grab the stuff I can’t get to. http://robohub.org/ lead me here after seeing Sabine Hauert do a talk on micro-swarms.

Derek Haoyang Li, the founder of Squirrel AI Learning, is a serial entrepreneur who co-founded two publicly listed companies, and one of the companies has a market cap of $200 million. Squirrel AI Learning is the leading AI + education innovator and unicorn at the forefront of the K12 AI revolution. Within three years of its product release, Squirrel AI Learning has established more than 2,600+ learning centers in China and hosted the first series of human-vs-AI competitions in the Asia-Pacific region that proved the AI’s success. Squirrel AI Learning is recognized by Deloitte as one of the top 10 global AI enterprises with high growth. Squirrel AI Learning was also included in MIT Technology Review’s TR50 Smartest Companies in China list. Stanford Graduate School of Business has also published a case study on Squirrel AI Learning.