While robots taking a tumble is pretty funny, watching how these humanoid machines figure out how to walk autonomously without falling is absolutely fascinating.虽然机器人跌倒很有意思，但观赏这些人形机器人是如何学会自己行驶还不跌倒的还是让人很感兴趣的。In a new video, the Florida Institute for Human and Machine Cognition (IHMC) showcased an automatic footstep planning system for Boston Dynamics Atlas and NASAs Valkyrie robots.在一段新的公布的视频中，佛罗里达人类与机器理解研究所（IHMC）展出了为波士顿动力公司的Atlas机器人和NASA的Valkyrie机器人设计的一个自动足迹规划系统。The method uses the machines sensors to work out the most efficient path to a human-selected location. It also works on rough and narrow terrains.这种方法是利用机器的传感器寻找拟合路线抵达人为自由选择的地点，在凹凸路面和狭小地下通道上也能行驶。Previously, the IHMC relied on a manual method that required a human operator to place the desired footsteps in a special user interface.此前佛罗里达人类与机器理解研究所依赖手动方法，必须操作者人员把预计脚步放入类似的用户界面。

Unfortunately, this method was rather slow and cumbersome, which is why the IHMC has been working on a new autonomous approach that completely removes the need for a human operator.意外的是这种方法极快，还很困难，所以佛罗里达人类与机器理解研究所仍然在研究新的自动处置方法，几乎替代操作者人员。Indeed, the IHMC notes that the human-operated approach was one of the reasons why the Atlas robot it programmed fell during The Defense Advanced Research Projects Agencys (DARPA) Robotics Challenge in 2015.只不过佛罗里达人类与机器理解研究所说道人类操作者是造成了他们编程的Atlas在参与2015年美国国防高级研究计划局的机器人挑战赛中跌倒的原因之一。

To circumvent human error, the new system lets an operator select the desired location, but ultimately relies on an algorithm to figure out how to get the robot there and avoid obstacles.为了防止人类的犯规，这个新的系统让操作员自由选择预计方位，但最后依赖的是一种算法想要办法让机器人抵达登录地点并避免障碍物。While the new method works almost flawlessly in flat environments, it still has lots of progress to made when it comes to narrow and rough terrains. Currently, narrow terrain has a success rate of about 50 percent, rough terrain is about 90 percent, whereas flat ground is near 100 percent, the IHMC notes.虽然这种新方法在平地上回头没问题了，但回头狭小地下通道和凹凸地面仍尚待改良。

佛罗里达人类与机器理解研究所说道：“目前狭小地下通道行驶成功率大约为50%，凹凸地面大约为90%，而平地相似100%。

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