DOI：10.1038/nature14422 中国科研用户发表 作者：Antoine Cully
摘要 : 本期封面所示为一条右前腿断了的一个机器人。为了在受到这种损伤的情况下还能行走，该机器人执行一个“智能试错算法”，该算法根据以前的(由刺激产生的)经验来寻找仍然有效的行为。
本期封面所示为一条右前腿断了的一个机器人。为了在受到这种损伤的情况下还能行走，该机器人执行一个“智能试错算法”，该算法根据以前的(由刺激产生的)经验来寻找仍然有效的行为。自主移动机器人在太空、深海或灾区等遥远或恶劣环境中将会极为有用。一个尚未解决的挑战是，让这种机器人在受损后能够恢复。Jean-Baptiste Mouret及同事开发了一个机器学习算法，它能让受损的机器人快速重新获得执行任务的能力。当它们受损时，如腿断了时甚或腿没了时，这种机器人会采用一个智能试错方法，来尝试它们经过计算认为具有潜在高性能的可能行为。在经过几次这种实验之后，它们会在不到两分钟时间内发现一种在此种受损情况下仍然有效的补偿行为。封面图片：Antoine Cully/ Pierre & Marie Curie University。插页: Alexander Vail。
Robots have transformed many industries, most notably manufacturing, and have the power to deliver tremendous benefits to society, such as in search and rescue, disaster response, health care and transportation. They are also invaluable tools for scientific exploration in environments inaccessible to humans, from distant planetsto deep oceans. A major obstacle to their widespread adoption in more complex environments outside factories is their fragility. wheras animals can quickly adapt to injuries, current robots cannot ‘think outside the box’ to find a compensatory behaviour when they are damaged: they are limited to their pre-specified self-sensing abilities, can diagnose only anticipated failure modes, and require a pre-programmed contingency plan for every type of potential damage, an impracticality for complex robots A promising approach to reducing robot fragility involves having robots learn appropriate behaviours in response to damage, but current techniques are slow even with small, constrained search spaces. Here we introduce an intelligent trial-and-error algorithm that allows robots to adapt to damage in less than two minutes in large search spaces without requiring self-diagnosis or pre-specified contingency plans. Before the robot is deployed, it uses a novel technique to create a detailed map of the space of high-performing behaviours. This map represents the robot’s prior knowledge about what behaviours it can perform and their value. When the robot is damaged, it uses this prior knowledge to guide a trial-and-error learning algorithm that conducts intelligent experiments to rapidly discover a behaviour that compensates for the damage. Experiments reveal successful adaptations for a legged robot injured in five different ways, including damaged, broken, and missing legs, and for a robotic arm with joints broken in 14 different ways. This new algorithm will enable more robust, effective, autonomous robots, and may shed light on the principles that animals use to adapt to injury.
来源： Nature 浏览次数：4