把握探究性学习的启示Exploratory Learning of Grasp Affordances |
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课程网址: | http://videolectures.net/rss2010_piater_elg/ |
主讲教师: | Justus H. Piater |
开课单位: | 因斯布鲁克大学 |
开课时间: | 2010-11-08 |
课程语种: | 英语 |
中文简介: | 掌握已知物体是自主机器人的许多重要应用的基础。在这里,考虑到现实世界的复杂性和与物理操纵相关的不确定性,主动学习具有很多前景。为此,我们开发了可交互的可学习对象表示。对象和相关联的动作参数由马尔可夫网络联合表示,其边缘电势编码3D中局部特征之间的成对空间关系。局部特征通常对应于视觉签名,但也可以表示与动作相关的参数,例如对于抓取对象有用的对象相对抓取器姿势。因此,在单个概率推理过程中统一检测,识别和合成用于已知对象的抓握。学习这些表示是一个两步的过程。首先,视觉对象模型通过机器人与其环境的类似游戏,自主,探索性的交互来学习。其次,通过类似游戏的交互逐渐获得特定于对象的抓取技能。结果是一个自主系统,它自动获取有关对象的知识以及如何检测,识别和掌握它们。 |
课程简介: | Grasping known objects is a capablity fundamental to many important applications of autonomous robotics. Here, active learning holds a lot of promise given the complexities of the real world and the uncertainties associated with physical manipulation. To this end, we have developed learnable object representations for interaction. Objects and associated action parameters are jointly represented by Markov networks whose edge potentials encode pairwise spatial relationships between local features in 3D. Local features typically correspond to visual signatures, but may also represent action-relevant parameters such as object-relative gripper poses useful for grasping the object. Thus, detecting, recognizing and synthesizing grasps for known objects is unified within a single probabilistic inference procedure. Learning these representations is a two-step procedure. First, visual object models are learned by play-like, autonomous, exploratory interaction of a robot with its environment. Secondly, object-specific grasping skills are incrementally acquired, again by play-like interaction. The result is an autonomous system that autonomously acquires knowledge about objects and how to detect, recognize and grasp them. |
关 键 词: | 自主机器人; 电位编码; 自治系统 |
课程来源: | 视频讲座网 |
最后编审: | 2020-06-28:yumf |
阅读次数: | 40 |