人工智能机器人已经开始学习折衣服和袜子、驾驶直升机以及缝合，但要能成为机器人管家、飞行员和外科医生，仍然超越机器人现有的能力范围之外。这是人工智能研究学者在日前嵌入式视觉高峰会议(Embedded Vision Summit)上一场专题演讲中所提出的结论。
“有 关机器人的研究存在许多限制，同时也还有诸多问题正待解决，”美国加州大学柏克莱大学工程系副教授Pieter Abbeel透过视讯介绍他的研究团队至今所取得的重大研究进展。“现在我们透过实地示范来指导机器人，但如果能让他们用YouTube等视频以实现自我学习的话，效果会更好。”
目前的研究人员们通常会教机器人完成一项大约20-30秒的任务。他们的目标是实现分阶段的规划系 统，让机器人能自行将较大任务区分成较小的任务逐一完成。但在有关机器人如何认识物体而不必求助于课程学习，以及如何用或然率来为移动的空间绘制地图等等 任务，则需要更多的研究进展与突破。
尽管存在限制，Abbeel展示直升机机器人能够执行一连串令人印象深刻的高难度动作，包括定点翻转与侧滚等。他说，“只要是飞行员能做的任何事情，我们的机器人系统就能透过学习实现，甚至还能比飞行员做得更精确且具有可重复性。”
Abbeel的研究团队采用人类飞行员的多次飞行资料，为机器人建立了学习模型。同时还采用了传统隐藏马可夫模型(HMM)以及卡尔曼滤波器来琢磨提升这些模型。机器人们仍然必须透过各种短期课程，分别学习每一项动作。
为了突破人工智能应用于机器人手术时面临的挑战，该研究团队还开发出教导机器人如何进行外科手术缝合的方法。
美国的外科医生每年指导机器人系统进行缝合逾30万次，但至今机器人自主缝合的试验上大约只有一半的次数能实现成功。Abbeel说：“结果虽然还不算太坏，但还不到能实际应用于手术的时候。”
Abbeel赞许在其实验室中所用的Willow Garage公司PR2 机器人，“这些机器人真的很棒，因为他们相当可靠，也十分适用于实验上。”
Abbeel的研究团队展示机器人会折毛巾和袜子等简单家务
CDgesmc
本文授权编译自EE Times，版权所有，谢绝转载
编译：Susan Hong
参考英文原文：DESIGN West: Robots study for butler role，by Rick Merritt
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DESIGN West: Robots study for butler role
Rick Merritt
SAN JOSE, Calif. – Robots are learning to fold socks, fly helicopters and sew, but the robotic butler, pilot and surgeon are still beyond the horizon. That was the conclusion of a keynote from a leading researcher in artificial intelligence at the Embedded Vision Summit here.
“There are a lot of limitations and a lot to be resolved here,” said Pieter Abbeel, an assistant professor of engineering at the University of California at Berkeley, after showing videos of many advances from his team. “Now we guide robots through demos, but it would better to let them use YouTube videos to learn on their own,” Abbeel said.
Researchers typically teach robots 20-30 second tasks today. They aim to enable hierarchical planning systems so robots can break down large jobs into smaller tasks themselves, he said. Other advances are needed in how robots perceive objects without resorting to learned classes and how they use probability to map the space in which they act, he said.
Despite the limits, Abbeel showed robotic helicopters executing an impressive series of difficult maneuvers including stationary flips and rolls. “Anything a pilot can do, our system can learn, and it can go beyond a pilot in being more accurate and repeatable,” he said.
Abbeel’s team used data from flights of multiple human pilots to create models. It applied hidden Markov models and Kalman filters to refine the models. Still the robots needed to learn each move individually in separate short sessions.
The team also developed methods to teach robots to tie a wide variety of knots and sew a few stitches on large fabrics. The work was in response to a challenge from a colleague to apply artificial intelligence to robotic surgery.
U.S. surgeons guide robotic systems to tie 300,000 knots a year, but so far experiments in autonomous suturing only succeed half the time. “That’s not bad, but not the kind of thing you want to go into surgery with,” he joked.
Abbeel praised the PR2 robots from Willow Garage he uses in the lab. “They are amazing because they are so reliable and so good to do experiments with,” he said.
Doing the laundry: Abbeel’s team showed robots folding towels and socks
责编：Quentin
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