Miscellaneous

The computer engineering class at my highschool designed and built robots to shoot pucks in a curling-style game. The catch was that there was 4 spots you could shot from and 4 robots shooting at the same time.

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Dynamic Solutions Curling Robot Button Masher Facing Off against The Shark For the Saint Mary's University Title.

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On the ice, a machine-learning system often triumphed over high-level South Korean players

Artificial intelligence still needs to bridge the “sim-to-real” gap. Deep-learning techniques that are all the rage in AI log superlative performances in mastering cerebral games, including chess and Go, both of which can be played on a computer. But translating simulations to the physical world remains a bigger challenge.

A robot named Curly that uses “deep reinforcement learning”—making improvements as it corrects its own errors—came out on top in three of four games against top-ranked human opponents from South Korean teams that included a women’s team and a reserve squad for the national wheelchair team. (No brooms were used).

One crucial finding was that the AI system demonstrated its ability to adapt to changing ice conditions. “These results indicate that the gap between physics-based simulators and the real world can be narrowed,” the joint South Korean-German research team wrote in Science Robotics on September 23.

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