A Review of Robot Learning for Manipulation: Challenges, Representations, and Algorithms
A Review of Robot Learning for Manipulation: Challenges, Representations, and Algorithms
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发表时间：
2019-07
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通讯作者：
Oliver Kroemer;S. Niekum;G. Konidaris
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作者：
Oliver Kroemer;S. Niekum;G. Konidaris
智能机器人的一个关键挑战是创造能够直接与周围世界互动以实现目标的机器人。过去十年，机器人操作问题的研究取得了实质性增长，其目的是利用日益增加的价格实惠的机器人手臂和抓手来制造能够直接与世界互动以实现其目标的机器人。学习将是这种自主系统的核心，因为真实的世界包含太多的变化，机器人无法期望预先拥有其环境、其中的物体或操纵它们所需的技能的准确模型。我们的目标是调查使用机器学习进行操作的研究的一个代表性子集。我们描述了机器人操作学习问题的形式化，将现有的研究综合成一个连贯的框架，并突出了许多剩余的研究机会和挑战。
A key challenge in intelligent robotics is creating robots that are capable of directly interacting with the world around them to achieve their goals. The last decade has seen substantial growth in research on the problem of robot manipulation, which aims to exploit the increasing availability of affordable robot arms and grippers to create robots capable of directly interacting with the world to achieve their goals. Learning will be central to such autonomous systems, as the real world contains too much variation for a robot to expect to have an accurate model of its environment, the objects in it, or the skills required to manipulate them, in advance. We aim to survey a representative subset of that research which uses machine learning for manipulation. We describe a formalization of the robot manipulation learning problem that synthesizes existing research into a single coherent framework and highlight the many remaining research opportunities and challenges.