Predictive action selector for generating meaningful robot behaviour from minimum amount of samples

机译：预测动作选择器，可从最少的样本中生成有意义的机器人行为

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摘要

Our aim is to better understand the action selection process of intelligent systems by looking at their ability of internal prediction. In robotic systems, one problem is to generate meaningful robot behaviour with a very small and simple set of trained motions. An additional problem is to compensate for incomplete sensory data while generating behaviour. We propose a new predictive action selector to contribute to the solution of these problems. Our action selector predicts task-relevant feature and motion sequences, and uses the prediction results to select the robot action. We validate our implemented model on a humanoid robot. The robot generates meaningful behaviour composed out of very simple and few trained motions, and at the same time it compensates for incomplete sensory data such as temporary loss of task-relevant visual features.

机译：我们的目标是通过了解智能系统的内部预测能力来更好地理解它们的动作选择过程。在机器人系统中，一个问题是通过一组非常简单的受过训练的动作来产生有意义的机器人行为。另一个问题是在产生行为时补偿不完整的感觉数据。我们提出了一种新的预测动作选择器，以帮助解决这些问题。我们的动作选择器可以预测与任务相关的功能和动作序列，并使用预测结果来选择机器人动作。我们在人形机器人上验证了我们的实现模型。机器人会产生非常有意义的行为，这些行为由非常简单且很少受过训练的动作组成，同时还能补偿不完整的感觉数据，例如与任务相关的视觉特征的暂时丢失。

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来源
《The Fourth joint IEEE international conferences on development and learning and epigenetic robotics》|2014年|139-145|共7页
会议地点 Genoa(IT)
作者
Wieser Erhard; Cheng Gordon;
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作者单位

Institute for Cognitive Systems, Technische Universität München, Germany;

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会议组织
原文格式 PDF
正文语种 eng
中图分类
关键词
Context; Joints; Robot sensing systems; Training; Vectors; Visualization; action selection; emergent behaviour; internal prediction;

机译：上下文;关节;机器人传感系统;训练;矢量;可视化;动作选择;紧急行为;内部预测;;

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Predictive action selector for generating meaningful robot behaviour from minimum amount of samples

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