Cleaning Robot Operation Decision Based on Causal Reasoning and Attribute Learning
Yapeng Li, Dongbo Zhang, Feng Yin, Ying Zhang
Abstract
In order to improve the operation ability of cleaning robots, this paper proposes a decision method for cleaning robot’s operation mode. Firstly, we use the hierarchical expression ability of deep network to obtain the attributes of garbage such as state, shape, distribution, size and so on. Then the causal relationship between the attributes and the operation modes can be built by using joint learning of association attributes with depth network model and causal inference. Based on this, a fuzzy inference decision network is designed. With the help of causal analysis, the structure of the decision model is greatly simplified. Compared with conventional fuzzy neural networks, the total parameters of the model are reduced by 2 / 3. The method proposed in this paper imitates the way that human dispose of different types of garbage and has good interpretability. The experimental results verify the effectiveness of the proposed method.
BibTeX
@inproceedings{iros2020_cleaningrobotope,
title = {Cleaning Robot Operation Decision Based on Causal Reasoning and Attribute Learning},
author = {Yapeng Li and Dongbo Zhang and Feng Yin and Ying Zhang},
booktitle = {IROS 2020},
year = {2020}
}