休·杰克曼

2023年6月27日

在2011年的电影《铁甲钢拳》的最后战斗场景,两个巨型机器人战斗在失去一个拳击环和失败者。休·杰克曼的性格,最后一轮之前,查理,带着他的机器人拉到一边,告诉它在一旁看着他,追随他的领导。决赛开始,查理把他保护起来,并且正确的moment-throws意味着上钩拳。他的机器人模拟相同的运动,摇摆机器人更大的困难。两人然后释放一系列重击对手史诗回归在回合结束之前,就在失败者可以完整的淘汰赛。灵感来自这个场景,研究人员创建了一个类似的遥控机器人,称为QIBBOT,模仿人类的真正运动斗士。这个机器人不仅可以包一拳,它以前所未有的速度。12毫秒的延迟,可能最快的遥控机器人创建迄今为止,其创造者声明。研究人员然后将QIBBOT测试一个AI-guided对手,学习和适应而打架。你可以在这里观看战斗:世界上最快的通过[大机器人战斗游戏与虚拟控制器:QIBBOT] Qibo机器人公司伊宁耿(詹姆斯)在威海Qibo机器人公司的首席执行官,中国领导QIBBOT的发展。 “Taking inspiration from the film [“Real Steel”] and incentivized by the entertainment value, we decided to see if we can realize the concept of real-life fighting robots,” he says. “We thought players’ experience with big and real robots would be very new and different from computer games.” Several different teleoperated robots already exist , but these have completely different purposes, and most tend to be small and only move at slow or medium speeds. “Fast speed is the first priority of fighting robots,” Geng says. With teleoperated robots, there are a lot of different aspects that factor into their speed, including communication, interface, actuation, transmission, the controller, and computation. To help QIBBOT achieve high speeds, Geng’s team focused on addressing issues related to the robot’s mechanics and controller. They first built an accurate kinematics and dynamics model, which they used to optimize QIBBOT’s mass distribution, mechanical structure, actuation, and communications. Then, in addition to using a conventional feedback controller that works reactively, they designed a new feedforward controller, which proactively responds to the motion commands from the VR controller. This approach cancels some of the latency caused by other components in the system, according to Geng. To create a formidable robot opponent that is guided by AI, Geng and his colleagues combined several different existing AI programs. Collectively, these programs help the autonomous AI-guided opponent distinguish between attack and defense, generate fighting strategies, and reference a library of data on the parameters for specific fighting scenarios. As seen in the video above, the AI opponent can learn moves while in the midst of a fight with the robot guided by Geng. While the battle is entertaining and undoubtedly looks fun, there are still some aspects of QIBBOT that need to be addressed. “We optimized the mechanical system design of QIBBOT for fast response, and that has a price—the accuracy,” says Geng. He notes that although accuracy may not matter as much in a fight scenario, it would be important if this tech was adapted for other purposes. He says QIBBOT is just a prototype for testing the design and controller. “It has some problems, like unwanted vibration, unnatural motion styles, et cetera. We are designing a new robot that will have dual arms and more joints in the body and arms, and will solve these problems.”