The Thunderhill Raceway in California’s Sacramento Valley is soaked in the oil of motorsport history.

加州圣克拉门托(Sacramento)的雷山赛道(Thunderhill Raceway)是摩托车运动的历史圣地。

It is home to the longest automobile race in the United States, known as the 25 Hours of Thunderhill, and last September, it was host to a very different kind of race: a race between man and machine.

这里是美国耗时最长的摩托车比赛——雷山25小时拉力赛(25 Hours of Thunderhill)的举办地。而在去年9月,这里举办了一次非常特殊的比赛:真人赛车手和机器人赛车手之间的比赛。

In the distance, a motorbike driver took the curves of the course like any other professional rider. It was only up close that reality hit the spectator: the rider wasn’t human. It was a blue humanoid robot that looked like it had stepped straight out of a screenshot from the computer game Halo.


Halo is set in an interstellar war between humanity and a bunch of aliens called the Covenant; you get the picture. When this robot – Motobot ?– stops driving, you feel like it could climb off the bike and hunt you down as well – but it can’t, yet.


Motobot 2.0 is a fully autonomous motorcycle-riding robot that was specially designed to drive around a racetrack at high speed on a Yamaha YZF-R1M, the same kind of motorbike that racing legend Valentino Rossi rides. Human operators can specify how aggressively Motobot should race on a scale of zero to 100% and it does the rest. This process is roughly parallel to how a racing team might discuss strategy with a human rider. The bike itself looks like the classic modern aerodynamic racing bike that overtakes you on the motorway.

Motobot 2.0是一台全自主摩托车驾驶机器人,它可以驾驶一辆雅马哈YZF-R1M高速行驶。传奇车手瓦伦迪诺.罗西(Valentino Rossi)驾驶的也是这款摩托车。人类操控员只需设定好Motobot的驾驶激烈度——从0到100%,其余的事情全都由它自行完成,过程类似于比赛支持团队与人类骑手讨论比赛策略。摩托车看上去就是一台能够在赛道上迅速超越你的经典现代流线型赛车。

In September, the team that developed Motobot 2.0 achieved one of its goals when the robot successfully hit speeds exceeding 200km/h (124 mph) on the race track – 50 km/h faster than its predecessor Motobot 1.0. Unfortunately, or fortunately, depending on your point of view, this still fell short of the lap time of Valentino Rossi – which they were trying to beat – by about 30 seconds.

9月,Motobot 2.0的开发团队在赛道上成功实现了时速200公里的阶段性目标(比其前身Motobot 1.0的时速快了50公里)。然而不幸却又幸运的是(看你角度如何),跑完一圈赛道所用时间仍然比假想敌——瓦伦迪诺.罗西要长30秒。

Rossi is one of the most successful motorcycle racers of all time, with nine Grand Prix World Championships to his name. His best lap time on the course was a very quick 85 seconds. Motobot tried to break Rossi’s record a second time in October – and failed again.

作为有史以来最为成功的摩托车赛车手之一,罗西共赢得九座世界摩托车大奖赛(Grand Prix World Championships)奖杯。他的最快赛道记录是惊人的85秒。Motobot在10月第二次尝试打破罗西的记录,不过依旧失败了。

“We had several major crashes, two were catastrophic,” says Hiroshi Saijou, CEO and managing director at Yamaha Motor Ventures & Laboratory Silicon Valley, and the lead on the Motobot project. “Nobody was injured, and both happened in safe, to human, controlled circumstances. Several times we’ve had low-speed incidents as we were developing and testing Motobot. This is all part of the price of pushing the boundaries of technology.”

我们撞过好几次车,其中有两次非常严重,雅马哈汽车投资公司和实验室硅谷公司CEO兼董事总经理西条浩司(Hiroshi Saijou)说,他同时担任Motobot项目主管。"事故中没人受伤,事故时,摩托车和机器人处于对人安全的受控状态。在我们开发测试Motobot的过程中还发生了几次低速事故。这些事故都属于探索未知科技领域时必须付出的代价。"

Motobot started life in 2014 as a ‘moonshot’ project for Japanese motorcycle manufacturer Yamaha. Moonshots are those “it would be great if they worked but…” projects: ambitious, exploratory and ground-breaking but with little hope of short-term profitability.


Yamaha’s initial concept was a “humanoid robot that can ride a motorcycle autonomously” and the company teamed up with SRI International to achieve its vision. SRI, the Stanford Research Institute, as it was originally known, was founded in 1946 to be the cutting edge of innovation in Silicon Valley. The institute has been responsible for the development of such projects as Apple assistant Siri, the computer mouse and humanoid robots such as Proxi, which is designed to assist humans after a natural disaster. Back in 1966 they built the first mobile robot with the ability to perceive and reason about its surroundings.

雅马哈的最初想法是开发一台"能自主驾驶摩托车的类人机器人",并与SRI国际研究所合作实现这一目标。始建于1946年的SRI全名为斯坦福研究所(Stanford Research Institute),一直处在硅谷科技研发的最前沿。研究所曾经的开发成果包括苹果语音助理Siri、计算机鼠标和能够在自然灾害后协助救灾的Proxi等类人机器人。早在1966年,他们就制成了能够感知周围环境并做出推理的可移动机器人。

“Why a motorbike?” ponders Hiroshi Saijou. “Because it is very difficult to do, and it had never been done before.”


“The significance of the 200km/h goal was that it requires extremely high-speed prevision computing. Calculations must take place at 1/1000 of a second – and a minor mistake would be amplified and impossible for Motobot to recover from.


“Most human riders do not have the experience to ride at this speed. So, we set this speed as a good enough target to show that Motobot’s abilities were superior to human’s. Beating Rossi would have been clear evidence that Motobot can perform beyond human capabilities.”


For Brian Foster, robotics engineer and Motobot project lead at SRI International, another goal of the project was to “learn what makes a great rider.

对机器人工程师、SRI国际研究所Motobot项目主管布莱恩.福斯特(Brian Foster)而言,项目的另一个目标在于"了解如何才能成为一名伟大的赛车手。"

“How riders sense the limits of traction, optimise power output of a bike and recover from exceeding the bike’s limits without crashing,” he says. “Using an unmodified bike was key to this and set the playing field for evaluating the robot versus the human competition.”


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.


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.


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.


“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.


“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.


“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.”


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.


“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.


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.


“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.


“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.”


Perhaps Motobot needs a jetpack to beat Rossi.


“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.”


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.”


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


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.


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.


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.


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.