人形機器人又有重大突破!多謝IHMC

While robots taking a tumble is pretty funny, watching how these humanoid machines figure out how to walk autonomously without falling is absolutely fascinating.

雖然機器人摔倒很有趣，但觀看這些人形機器人是如何學會自己行走還不摔倒的還是讓人很感興趣的。

In a new video, the Florida Institute for Human and Machine Cognition (IHMC) showcased an automatic footstep planning system for Boston Dynamics' Atlas and NASA's Valkyrie robots.

在一段新發布的視頻中，佛羅里達人類與機器認知研究所（IHMC）展示了爲波士頓動力公司的Atlas機器人和NASA的Valkyrie機器人設計的一個自動足跡規劃系統。

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.

這種方法是利用機器的傳感器找到最優路線到達人爲選擇的地點，在凹凸路面和狹窄通道上也能行走。

Previously, the IHMC relied on a manual method that required a human operator to place the desired footsteps in a special user interface.

此前佛羅里達人類與機器認知研究所依靠手動方法，需要操作人員把預定腳步放進特殊的用戶界面。

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.

不幸的是這種方法很慢，還很麻煩，所以佛羅里達人類與機器認知研究所一直在研究新的自動處理方法，完全替代操作人員。

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 Agency's (DARPA) Robotics Challenge in 2015.

其實佛羅里達人類與機器認知研究所說人類操作是導致了他們編程的Atlas在參加2015年美國國防高級研究計劃局的機器人挑戰賽中摔倒的原因之一。

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.

爲了避免人類的失誤，這個新系統讓操作員選擇預定位置，但最終依靠的是一種算法想辦法讓機器人到達指定地點並避開障礙物。

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.

雖然這種新方法在平地上走沒問題了，但走狹窄通道和凹凸地面仍有待改進。佛羅里達人類與機器認知研究所說：“目前狹窄通道行走成功率約爲50%，凹凸地面約爲90%，而平地接近100%。”