Weight-aware robot motion planning for lift-to-pass action
Weight-aware robot motion planning for lift-to-pass action
复制标题
用于提升通过动作的重量感知机器人运动规划
DOI：
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发表时间：
2014
期刊：
影响因子：
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通讯作者：
G. Sandini
中科院分区：
文献类型：
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作者：
Oskar Palinko;A. Sciutti;F. Rea;G. Sandini
在两个人之间传递物体是一个非常自然和无缝的操作，这主要归功于促进这一过程的非语言线索。仅仅通过动作观察，人类就可以很容易地预测到一个经过的运动将在哪里和何时结束，以及被运输的物体有多重。但是，这种自然的理解怎么可能被移植到非人类特工身上呢？我们介绍了一种简单的机器人体系结构，使iCub类人机器人能够视觉识别物体的重量，并选择一种隐含地将此类信息传达给动作伙伴的提举传递运动。在这项工作中，我们主要关注存储物体质量与其视觉外观之间的联系所需的程序记忆模块的建立和训练，并提出如何使用该模型来连续选择可交流的举重动作。
Passing an object between two humans is a very natural and seamless operation, mainly thanks to non-verbal cues which facilitate the process. Just from action observation, humans can easily anticipate where and when a passing movement will end and how heavy the transported object is. But how could this natural understanding be ported to non-human agents? We introduce a simple robotic architecture to enable the iCub humanoid robot to visually recognize the weight of an object and select a lift-to-pass motion which implicitly communicates such information to the action partner. In this work we mainly focus on the building and training of the procedural memory module needed to store the association between the mass of an object and its visual appearance, and we propose how such a model can be used to successively select communicative lifting motions.