This rule meant that the designers then had to deal with constraints on all sorts of things like geometry, the size of the actuators that control the robot’s movements, where sensors were placed and more factors that wouldn’t have been an issue in a purpose-built vehicle. Yes, Motobot was physically attached to the bike but its hand was still required to grip and twist the throttle.

　　这一规则意味着，设计师必须克服外形尺寸、控制机器人运动的促动器的型号、传感器位置以及其他对于特制车辆而言不成问题的限制性因素。没错，设计师把Motobot固定在了摩托车上，但是它的手却仍然需要抓牢并扭动油门开关。

　　On the other hand, the robot didn’t need to use cameras or lasers to navigate like an autonomous car would because it wasn’t a public road. It could use technologies like the simpler GPS and IMUs (inertial measurement unit), that are often used to control such things as drones and satellites.

　　另一方面，由于机器人不在开放公路上行驶，因此它无需像自主驾驶汽车那样使用摄像头或激光进行导航，而是使用GPS或IMU(惯性导航系统)导航，这两种导航技术常常用来控制无人机和卫星。

　　There were, however, plenty of challenges for the engineers to face before the robot could ride a bike very fast around the track without crashing it.

　　然而，工程师们必须克服很多挑战，才让机器人能够驾驶摩托车在赛道上高速安全地飞驰。

　　“Our first big challenge was the balance controller,” says Foster. “Motobot had to be taught how to balance the bike at lean angles from zero to over 50 degrees, at speeds between 5km/h and over 200 km/h. And it had to be able to change bank angles rapidly and precisely. The control algorithms to do this were constantly refined as we got close to our final high-performance version.

　　我们最初面对的挑战就是平衡控制器，福斯特说。"我们必须教育Motobot如何在车身严重倾斜——倾角从0到50度，时速五公里到200公里以上的情况下保持平衡，它必须能够迅速而精确地调整侧倾角。随着最终的高性能版本接近完成，控制算法也必须不断优化。"

　　“Similarly, the path-following algorithm had to work well at high-speed straights, sweeping turns, hairpin turns, strong acceleration, and strong deceleration. Developing a controller that was adaptive to such a wide range of extreme conditions was a huge challenge.

　　同样，路线跟随算法必须在高速直线道、缓弯道、急弯道、急加速和急减速等条件下正确运行。开发能够适应如此复杂极端条件的控制器是一项巨大的挑战。

　　“From my perspective though, the biggest challenge was identifying performance limits without crashing,” Foster adds. “To improve the algorithms, we had to constantly push it to the limit to see where improvements were needed. If we pushed too hard, we could crash and lose everything. If we didn’t push hard enough, we wouldn’t learn enough, and our progress would be too slow. It was a constant risk balance exercise.”

　　我认为最大的挑战在于：在不翻车的前提下寻找性能极限，福斯特说。"为了改进算法，我们需要不断测试性能极限，从而了解哪些地方需要改进。如果我们超过极限，就必然会翻车，毁掉一切。如果我们过于保守，了解的就必然有限，也很难进步。这是一个不断测试风险平衡的游戏。"

　　To try to reduce the risk, Foster and his team would bring Motobot and the bike into the lab, where they ran a very sophisticated simulation whereby the robot would apply brakes and shift gears as if it were racing on the track. The sensors would then feed the data back into the simulation hundreds of times per second.

　　为了降低风险，福斯特和他的团队把Motobot带进实验室进行极端复杂的仿真试验，模拟在赛道上刹车和换挡等操作。各个传感器每秒可把数百条数据输入模拟器。

　　“Ultimately, nothing perfectly replicates the real world, so we still needed a lot of track time and had to manage the risks that come with that,” Foster says.

　　我们最终发现，任何模拟器也无法完美复制真实的赛道，因此我们仍然需要在赛道上进行大量测试，并且有效控制赛道风险，福斯特说。

　　Hiroshi Saijou thinks the “cost to learn” is the reason why we didn’t see any depressing headlines about AI beating another human world champion.

　　西条浩司认为，正是由于"学习成本"的存在，我们才从未看到过人工智能在赛道上击败人类赛车手的新闻。

　　“The most significant one is the cost – not only money but time and resources - to learn,” he says. “AI for a board game, such as AlphaGo, can learn how to play and how to win pretty quickly since there is no risk of it getting damaged. I believe that there were millions of failures before it eventually won over a human champion.

　　最大的问题在于学习成本，不仅包括财务成本，还包括时间和各类资源，他说。"AlphaGo等棋类人工智能因为没有翻车被毁的风险，所以能很快学会如何下棋，如何获胜。我相信在它最终战胜人类顶级围棋手之前必然曾经经历过数百万次的失败。"

　　“For Motobot, the learning cost is way more expensive and repairs take a long time. So, we needed to take extraordinary care each time we did a trial.”

　　而Motobot的学习成本则更加高昂，修复破损的机器人需要花费很长时间。因此，我们每次测试都需要十分谨慎小心。

　　Perhaps Motobot needs a jetpack to beat Rossi.

　　Motobot可能需要装备喷气发动机才能战胜罗西。

　　“We went back and forth discussing what the limits to the competition should be,” says Stephen Morfey, a roboticist and now director of Morfey Design, a robot design consultancy. He was the lead mechanical designer on the Motobot project in its first phase and worked on other humanoid robots for SRI International like the walking bot Durus. “Jet thrusters weren’t allowed, but it could be aerodynamically shaped. We decided that physically attaching Motobot to the bike wasn’t cheating because its hands had to grip the handlebars.”

　　我们反复讨论比赛极限在哪里，机器人专家、机器人设计事务所"墨菲设计(Morfey Design)"主任史蒂芬.墨菲(Stephen Morfey)说。他曾经担任Motobot项目一期机械设计师，并曾为SRI国际研究所设计其他类人机器人，其中包括行走机器人Durus。"尽管不允许使用喷气发动机，但机器人可以进行空气动力力学优化。我们认定，由于Motobot的双手仍然需要紧握车把，因此通过机械手段把它固定在摩托车上不算作弊。"

　　“At the start of the project, controlling Motobot was like playing a video game,” he adds. “You set the speed and told it the direction you wanted it to go. By the end, after I had left, it was autonomous.”

　　项目开始时，控制Motobot就像是在玩电子游戏，他说。"你需要设定好速度，并告诉他你想去的方向。最后当我离开时，它已经成为自主机器人。"

　　It would have been much easier to beat Rossi, he thinks, by designing from scratch a very fast autonomous two-wheeled vehicle. “No, we didn’t beat Rossi. Why not? Because it is a hard problem,” he says. “There are hundreds of different variables that you must consider. In principle, you can get a robot to optimise all this stuff, but in practice, it is much harder.”

　　他认为，如果从零开始设计一台双轮自主机器人，打败罗西就会更容易。"不，我们没打败过罗西。为什么不这样做呢?因为我们需要解决更难的问题，"他说。"有数百个不同变量需要考虑。理论上你能设计出一台使所有参数都最优化的机器人，但实际中难度会更大。"

　　While the failure of Motobot to beat Rossi’s time may have dented the pride of the engineering team involved, important lessons were learned.

　　Motobot未能打败罗西，这无疑没能让工程团队为之欢欣鼓舞，但同时也使他们获得了重要的发现。

　　The future of Motobot, it seems, might be on two legs. Motobot is different from most humanoid robots because it doesn’t walk… yet. But future versions might be able to walk up to the bike and get on it.

　　似乎Motobot的未来取决于它的两条腿。Motobot和大多数类人机器人不同的是，它还不能行走。但是未来的版本将会自动走到摩托车旁并像人那样上车。

　　A kind of retrofitted autonomy, applicable to modern day problems, may have been made possible through their research and experiments. For example, in coming years, developing nations could use humanoid robots like Motobot to operate the perfectly good tractors and diggers that would have been replaced with new and expensive autonomous versions.

　　致力于解决当前问题的自动化技术设想将随着研究和实验的发展成为现实。例如，在未来，发展中国家可能会使用类似Motobot的类人机器人操控拖拉机和挖掘机，届时，还将有价格昂贵的新型自主化拖拉机和挖掘机问世。

　　SRI International is already working with Chilean mining company Enaex to develop a rather freaky-looking remotely operated robot called Robominer that has the head, two arms and torso of a humanoid robot on four wheels.

　　SRI国际研究所正与智力矿业企业Enaex合作开发一种名为"Robominer"造型怪异的远程遥控机器人。它拥有与类人机器人相似的脑袋、两条手臂和躯干，所不同的是它同时还有四只轮子。

　　Would Hiroshi Saijou classify Motobot a success? “It is still on the way. We have learned a lot in the last three years and will use this knowledge in our products in the future. It has huge potential for us to get real success in our business,” he says. “What we learned is so unique that it would have been hard to get without Motobot. We are actively working on Motobot 3.0. Please stay tuned.”

　　在西条浩司眼里，Motobot是否取得了成功?"它还在路上。我们在过去三年有了很多发现，这些发现会运用到未来产品的开发中。我们的业务取得巨大成功的潜力很大，"他说。"我们的发现十分独特，可以说没有Motobot就不会有这些发现。我们正在积极开发Motobot 3.0。请静候佳音。"

　　Rossi, you have been warned.

　　罗西，你的记录保持不了多久了。