A research team at MIT's Improbable Artificial Intelligence Lab, part of the Computer Science and Artificial Intelligence Laboratory (CSAIL), taught a Unitree Go1 quadruped to dribble a soccer ball on various terrains. DribbleBot can maneuver soccer balls on landscapes like sand, gravel, mud and snow, adapt its varied impact on the ball's motion and get up and recover the ball after falling. The team used simulation to teach the robot how to actuate its legs during dribbling. This allowed the robot to achieve hard-to-script skills for responding to diverse terrains much quicker than training in the real world. Because the team had to load its robot and other assets into the simulation and set physical parameters, they could simulate 4,000 versions of the quadruped in parallel in real-time, collecting data 4,000 times faster than using just one robot.

A team of researchers at Google's Deep Mind London project, has taught animated players how to play a realistic version of soccer on a computer screen. In their paper published in the journal Science Robotics, the group describes teaching the animated players to play as solo players and also in teams. For several years, robot engineers have been working diligently to create robots capable of playing soccer. Such work has resulted in competition between various groups to see who can devise the best robot players. And that has led to the creation of RoboCup, which has several leagues, both in the real world and simulated.

Artificial intelligence has learned to play soccer. By learning from decades worth of computer simulations, an AI took digital humanoids from flailing tots to proficient players. Researchers at the AI research company DeepMind taught the AI how to play soccer in a computer simulation through an athletic curriculum resembling a sped-up version of growing a human baby into a football player. The AI was given control over digital humanoids with realistic body mass and joint movements. "We don't put infants in a 11 versus 11 match," says Guy Lever at DeepMind.

Trailblazers Week celebrates the women who have pushed boundaries and paved the way for others in their industries. Manuela Veloso grew up in Portugal in the 1960s and '70s in a household where innovations, from the moon landing to the building of a huge bridge in Lisbon, were the subject of dinner-table discussion. In 1994, she moved to the U.S. to earn a master's degree in computer science, and she went on to get her Ph.D. at Carnegie Mellon. It was the golden era of artificial intelligence, the "years of deep thoughts, chess playing, hopping robots," she tells the Cut. Veloso spent more than two decades at the university, working her way up to become the head of its machine-learning department, and has been researching artificial intelligence ever since -- now as head of AI research at JPMorgan and professor emeritus at Carnegie Mellon.

This work presents an application of Reinforcement Learning (RL) for the complete control of real soccer robots of the IEEE Very Small Size Soccer (VSSS) [1], a traditional league in the Latin American Robotics Competition (LARC). In the VSSS league, two teams of three small robots play against each other. We propose a simulated environment in which continuous or discrete control policies can be trained, and a Sim-to-Real method to allow using the obtained policies to control a robot in the real world. The results show that the learned policies display a broad repertoire of behaviors which are difficult to specify by hand. This approach, called VSSS-RL, was able to beat the human-designed policy for the striker of the team ranked 3rd place in the 2018 LARC, in 1-vs-1 matches.

What makes the difference between a good soccer player and a great one? Players regularly train with GPS sensors strapped across their chests. Israeli sports-tech startup PlayerMaker makes a small device that tracks much more than GPS can. Worn on a player's shoes during training, its sensors and proprietary software detect every ball touch and build an accurate player "gait profile." "The sensors know if you make a pass, a run or interception," says CEO Guy Aharon.

Here's what AI can learn from biology. In building the world's first airplane at the dawn of the 20th century, the Wright Brothers took inspiration from the "insightful" movements of birds. They observed and reverse-engineered aspects of the wing in nature, which in turn helped them make important discoveries about aerodynamics and propulsion. Similarly, to build machines that think, why not seek inspiration from the three pounds of matter that operates between our ears? Geoffrey Hinton, a pioneer of artificial intelligence and winner of the Turing Award, seemed to agree: "I have always been convinced that the only way to get artificial intelligence to work is to do the computation in a way similar to the human brain."

Computers can beat humans at sophisticated tasks like the game Go, but can they also drive a car, ... [ ] speak languages, play soccer, and perform a myriad of other tasks like humans? Here's what AI can learn from biology. In building the world's first airplane at the dawn of the 20th century, the Wright Brothers took inspiration from the "insightful" movements of birds. They observed and reverse-engineered aspects of the wing in nature, which in turn helped them make important discoveries about aerodynamics and propulsion. Similarly, to build machines that think, why not seek inspiration from the three pounds of matter that operates between our ears? Geoffrey Hinton, a pioneer of artificial intelligence and winner of the Turing Award, seemed to agree: "I have always been convinced that the only way to get artificial intelligence to work is to do the computation in a way similar to the human brain."

Fox News Flash top headlines for Nov. 11 are here. Check out what's clicking on Foxnews.com The Massachusetts Institute of Technology (MIT) recently unveiled a new video of its Mini Cheetah robot, demonstrating that the four-legged android can now dribble a soccer ball, run and jump. In March, the Mini Cheetah robots were seen doing backflips. "Eventually, I'm hoping we could have a robotic dog race through an obstacle course, where each team controls a mini cheetah with different algorithms, and we can see which strategy is more effective," Sangbae Kim, Director of Biomimetic Robotics Lab at MIT, said at the time.

Autonomous robots which can play soccer could be the solution to solving an age-old traffic problem. The artificial intelligence that keeps the bots moving is being trialled as a new way to keep clearways in Sydney moving. The system works by teaching a machine to analyse historical traffic data and then learn how to identify when a car is causing a problem. "It's a system that would automatically detect that a vehicle blocking a clearway based on traffic data," said developer Jayen Ashar, the lead engineer at Clearway Tech. The system would use machine learning -- "specifically supervised learning", he said -- to learn the patterns from historical data and then apply those to traffic in the future.