机器人(英语:Robot)包括一切模拟人类行为或思想与模拟其他生物的机械(如机器狗,机器猫等)。狭义上对机器人的定义还有很多分类法及争议,有些电脑程序甚至也被称为机器人。在当代工业中,机器人指能自动运行任务的人造机器设备,用以取代或协助人类工作,一般会是机电设备,由计算机程序或是电子电路控制。

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机器学习已经成为近年来最流行的话题之一。我们今天看到的机器学习的应用只是冰山一角。机器学习革命才刚刚开始。它正在成为所有现代电子设备不可分割的一部分。在自动化领域的应用,如汽车、安全和监视、增强现实、智能家居、零售自动化和医疗保健,还不多。机器人技术也正在崛起,主宰自动化世界。机器学习在机器人领域的未来应用仍未被普通读者发现。因此,我们正在努力编写这本关于机器学习在机器人技术上的未来应用的编辑书籍,其中几个应用已经包含在单独的章节中。这本书的内容是技术性的。它试图覆盖机器学习的所有可能的应用领域。这本书将提供未来的愿景在未探索的领域的应用机器人使用机器学习。本书中提出的观点得到了原始研究结果的支持。本章在这里提供了所有必要的理论和数学计算的深入研究。对于外行人和开发人员来说,它将是完美的,因为它将结合高级材料和介绍性材料,形成一个论点,说明机器学习在未来可以实现什么。它将详细介绍未来的应用领域及其方法。因此,本书将极大地有利于学术界、研究人员和行业项目管理者开发他们的新项目,从而造福人类。

https://link.springer.com/book/10.1007/978-981-16-0598-7#about

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Consider a truck filled with boxes of varying size and unknown mass and an industrial robot with end-effectors that can unload multiple boxes from any reachable location. In this work, we investigate how would the robot with the help of a simulator, learn to maximize the number of boxes unloaded by each action. Most high-fidelity robotic simulators like ours are time-consuming. Therefore, we investigate the above learning problem with a focus on minimizing the number of simulation runs required. The optimal decision-making problem under this setting can be formulated as a multi-class classification problem. However, to obtain the outcome of any action requires us to run the time-consuming simulator, thereby restricting the amount of training data that can be collected. Thus, we need a data-efficient approach to learn the classifier and generalize it with a minimal amount of data. A high-fidelity physics-based simulator is common in general for complex manipulation tasks involving multi-body interactions. To this end, we train an optimal decision tree as the classifier, and for each branch of the decision tree, we reason about the confidence in the decision using a Probably Approximately Correct (PAC) framework to determine whether more simulator data will help reach a certain confidence level. This provides us with a mechanism to evaluate when simulation can be avoided for certain decisions, and when simulation will improve the decision making. For the truck unloading problem, our experiments show that a significant reduction in simulator runs can be achieved using the proposed method as compared to naively running the simulator to collect data to train equally performing decision trees.

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