Goto

Collaborating Authors

Robots


John Deere closes in on fully autonomous farming with latest AI acquisition

#artificialintelligence

John Deere is announcing the acquisition of a state-of-the-art algorithm package from artificial intelligence startup Light. For those of you wondering when driverless vehicles will truly begin to make their mark on society, the answer is: today. Up front: No, you won't be seeing green tractors rolling themselves down city streets anytime soon. But the timeline for fully autonomous farming is being massively accelerated. Today's purchase is all about John Deere's need for speed -- and accuracy, but first let's talk about rapid development.


Ethics in Robotics and Artificial Intelligence

#artificialintelligence

As robots are becoming increasingly intelligent and autonomous, from self-driving cars to assistive robots for vulnerable populations, important ethical questions inevitably emerge wherever and whenever such robots interact with humans and thereby impact human well-being. Questions that must be answered include whether such robots should be deployed in human societies in fairly unconstrained environments and what kinds of provisions are needed in robotic control systems to ensure that autonomous machines will not cause humans harms or at least minimize harm when it cannot be avoided. The goal of this specialty is to provide the first interdisciplinary forum for philosophers, psychologists, legal experts, AI researchers and roboticists to disseminate their work specifically targeting the ethical aspects of autonomous intelligent robots. Note that the conjunction of "AI and robotics" here indicates the journal's intended focus is on the ethics of intelligent autonomous robots, not the ethics of AI in general or the ethics of non-intelligent, non-autonomous machines. Examples of questions that we seek to address in this journal are: -- computational architectures for moral machines -- algorithms for moral reasoning, planning, and decision-making -- formal representations of moral principles in robots -- computational frameworks for robot ethics -- human perceptions and the social impact of moral machines -- legal aspects of developing and disseminating moral machines -- algorithms for learning and applying moral principles -- implications of robotic embodiment/physical presence in social space -- variance of ethical challenges across different contexts of human -robot interaction


Tiny robotic crab is smallest-ever remote-controlled walking robot: Smaller than a flea, robot can walk, bend, twist, turn and jump

#artificialintelligence

Just a half-millimeter wide, the tiny crabs can bend, twist, crawl, walk, turn and even jump. The researchers also developed millimeter-sized robots resembling inchworms, crickets and beetles. Although the research is exploratory at this point, the researchers believe their technology might bring the field closer to realizing micro-sized robots that can perform practical tasks inside tightly confined spaces. The research will be published on Wednesday (May 25) in the journal Science Robotics. Last September, the same team introduced a winged microchip that was the smallest-ever human-made flying structure.


Where the billions spent on autonomous vehicles by U.S. and Chinese giants is heading

#artificialintelligence

For years, Alphabet's Waymo and others leaders have promised autonomous vehicles are just around the bend. But that future has not arrived yet. "In one word, it's complexity," said James Peng, CEO and co-founder of Pony.ai, an autonomous vehicle company. "Every time there is a technical breakthrough, there are challenges. We have the AI, the fast computer chips, the sensors. Despite promises of life-saving, climate-change fighting, and cost-efficient driving, the reality is that "the autonomous vehicle nirvana is 10 years out," said Michael Dunne, CEO of autotech consultancy ZoZoGo. "While it's not impossible to get there, even the most advanced technologies are not there yet and used mainly in confined areas where things are predictable.


New deep learning technique paves path to pizza-making robots

#artificialintelligence

This article is part of our coverage of the latest in AI research. For humans, working with deformable objects is not significantly more difficult than handling rigid objects. We learn naturally to shape them, fold them, and manipulate them in different ways and still recognize them. But for robots and artificial intelligence systems, manipulating deformable objects present a huge challenge. Consider the series of steps that a robot must take to shape a ball of dough into pizza crusts.


The art of making robots – #ICRA2022 Day 2 interviews and video digest

Robohub

Every year, ICRA gathers an astonishing number of robot makers. With a quick look at the exhibitors, one can already perceive the immense creativity and inventiveness that creators put in their robots. To my eyes, creating a robot is an art: how does a robot should look like? What would it be good for? Should we go for legs or wheels?


Crabtastic! Adorable robotic crab tinier than a FLEA can bend, twist, crawl and even jump

Daily Mail - Science & tech

An adorable robotic crab has been developed by scientists – but you'll need a magnifying glass if you want to see it. The tiny bot is inspired by peekytoe crabs and measures just 0.02 inches (0.5mm) wide, making it the smallest ever remote-controlled walking robot. Despite being smaller than a flea, the robot can bend, twist, crawl, walk, turn, and even jump. Researchers from Northwestern University, who developed the robot, believe the bot could be used to perform a range of tasks in confined spaces. 'You might imagine micro-robots as agents to repair or assemble small structures or machines in industry or as surgical assistants to clear clogged arteries, to stop internal bleeding or to eliminate cancerous tumours -- all in minimally invasive procedures,' said Professor John Rogers, who led the project.


3 Innovative Ways You Can Use Robots

#artificialintelligence

Since its inception, the robotics industry has seen a tremendous hike in its revenue growth. The global robotics market is expected to see a CAGR of 24.52% within 2023. Realizing the incredible capabilities of robots, organizations worldwide are investing a lot of their capital to enjoy the benefits of this AI application. Grabbing the opportunity to enhance organizational productivity, automate business services, and stay unique in the crowd of competitors, organizations are finding potential areas where they could replace human tasks with robots. Robots have become one of the key contributors to driving the market revenue significantly.


Using AI to anticipate others' behavior on the road

#artificialintelligence

Humans may be one of the biggest roadblocks keeping fully autonomous vehicles off city streets. If a robot is going to navigate a vehicle safely through downtown Boston, it must be able to predict what nearby drivers, cyclists, and pedestrians are going to do next. Behavior prediction is a tough problem, however, and current artificial intelligence solutions are either too simplistic (they may assume pedestrians always walk in a straight line), too conservative (to avoid pedestrians, the robot just leaves the car in park), or can only forecast the next moves of one agent (roads typically carry many users at once.) MIT researchers have devised a deceptively simple solution to this complicated challenge. They break a multiagent behavior prediction problem into smaller pieces and tackle each one individually, so a computer can solve this complex task in real-time. Their behavior-prediction framework first guesses the relationships between two road users--which car, cyclist, or pedestrian has the right of way, and which agent will yield--and uses those relationships to predict future trajectories for multiple agents.


Roboticists go off road to compile data that could train self-driving ATVs: TartanDrive dataset likely largest for off-road environments

#artificialintelligence

They drove the heavily instrumented ATV aggressively at speeds up to 30 miles an hour. They slid through turns, took it up and down hills, and even got it stuck in the mud -- all while gathering data such as video, the speed of each wheel and the amount of suspension shock travel from seven types of sensors. The resulting dataset, called TartanDrive, includes about 200,000 of these real-world interactions. The researchers believe the data is the largest real-world, multimodal, off-road driving dataset, both in terms of the number of interactions and types of sensors. The five hours of data could be useful for training a self-driving vehicle to navigate off road.