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Watch a Spot robot from Boston Dynamics explore an old mine

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The ground is rocky and uneven. Old, rusted rails that used to carry loads of precious metals run the length of the path. Most wheeled robots would have trouble navigating this uneven surface, but it’s not a problem for Spot.

“This is one of the most advanced robots in the world.” Hao Zhang tells me. He’s a professor at the Colorado School of Mines, and he’s brought his department’s new robotic dog from Boston Dynamics to the Edgar Mine outside of Denver for testing. The school is one of the first customers to buy a Spot robot since the four-legged machines went on sale this summer.

Spot robot in Edgar Mine

A handler guides Spot the robotic dog with a proprietary tablet controller.


Agata Bogucka

Much of Zhang’s work in robotics involves exploring ways robots can take over dangerous jobs from people, like searching for survivors in a collapsed mine or inspecting nuclear facilities.

“It’s pretty amazing,” Zhang said. “I have been working on robots for more than 10 years, and we’ve never had such a robot that is so well designed that it can do a lot of things just out of the box.” 

Watch the video above to see how Spot handled its first test-run inside the mine.

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AI researchers challenge a robot to ride a skateboard in simulation

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AI researchers say they’ve created a framework for controlling four-legged robots that promises better energy efficiency and adaptability than more traditional model-based gait control of robotic legs. To demonstrate the robust nature of the framework that adjusts to conditions in real time, AI researchers made the system slip on frictionless surfaces to mimic a banana peel, ride a skateboard, and climb on a bridge while walking on a treadmill. An Nvidia spokesperson told VentureBeat that only the frictionless surface test was conducted in real life because of limits placed on office staff size due to COVID-19. The spokesperson said all other challenges took place in simulation. (Simulations are often used as training data for robotics systems before those systems are used in real life.)

“Our framework learns a controller that can adapt to challenging environmental changes on the fly, including novel scenarios not seen during training. The learned controller is up to 85% more energy-efficient and is more robust compared to baseline methods,” the paper reads. “At inference time, the high-level controller needs only evaluate a small multi-layer neural network, avoiding the use of an expensive model predictive control (MPC) strategy that might otherwise be required to optimize for long-term performance.”

The quadruped model is trained in simulation using a split-belt treadmill with two tracks that can change speed independently. That training in simulation is then transferred to a Laikago robot in the real world. Nvidia released video of simulations and laboratory work Monday, when it also unveiled AI-powered videoconferencing service Maxine and the Omniverse simulated environment for engineers in beta.

A paper detailing the framework for controlling quadruped legs was published a week ago on preprint repository arXiv. AI researchers from Nvidia; Caltech; University of Texas, Austin; and the Vector Institute at the University of Toronto contributed to the

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Curly the Curling Robot Can Beat the Pros at Their Own Game | Smart News

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The sport of curling requires such precision and strategy that it’s sometimes referred to as “chess on ice.” Players push 40-pound stones across frozen sheets, rotating the stones just enough that they “curl,” and try to knock opposing teams’ stones out of central rings.

Subtle variables at play—tiny, ever-changing bumps in ice, the pressure exerted by one’s hand, the smoothness of the stone—all impact the outcome, so much that curling requires machine-like precision from its players.

So, it makes sense that an actual machine might have a shot at winning, if it could learn to strategize on its own. Enter Curly: a robot powered by artificial intelligence (AI) that recently competed against professional South Korean curling teams and won three out of four official matches.

Curly’s impressive feat is recounted in an article published this month in Science Robotics by researchers Seong-Whan Lee and Dong-Ok Won of Korea University and Klaus-Robert Müller of the Berlin Institute of Technology. The robot gave a top-ranked women’s team and a national wheelchair team a run for their money, the authors write, thanks to its “adaptive deep reinforcement learning framework.”

Curly actually consists of two robots that communicate with each other: a “skipper” that aims the stone and a “thrower” that pushes it across the ice, reports Brooks Hays for United Press International (UPI). It rolls on wheels and uses a conveyer belt to rotate the curling stone, reports Matt Simon for Wired magazine. One camera on Curly’s “head” is able to give the robot a view of the field, and another camera just above its front wheels watches the “hogline,” or the boundary on the ice where players are required to release the stone.

When Curly competes, it raises its white, teardrop-shaped head and extends its seven-foot-long neck to get a good view