A deep Q-learning network based active object detection model with a novel training algorithm for service robots
A deep Q-learning network based active object detection model with a novel training algorithm for service robots
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基于深度 Q 学习网络的服务机器人主动目标检测模型和新颖的训练算法
DOI：
10.1631/fitee.2200109
复制
发表时间：
2022-09
影响因子：
3
通讯作者：
Xuyang Shao
中科院分区：
文献类型：
--
作者：
Shaopeng Liu;Guohui Tian;Yongcheng Cui;Xuyang Shao
本文主要研究活动目标检测(AOD)问题。AOD是服务机器人在家庭环境中完成任务的重要组成部分，它通过采取适当的移动动作引导机器人接近目标对象。目前的AOD方法大多是基于强化学习的，训练效率和测试精度都较低。因此，本文提出了一种基于深度Q学习网络(DQN)和一种新的训练算法的AOD模型。DQN模型是为适应各种动作的Q值而设计的，它包括状态空间、特征提取和多层感知器。与已有研究相比，本文针对DQN模型设计了一种新的基于记忆的训练算法，以提高训练效率和测试精度。此外，还提出了一种生成结束状态的方法，用于在训练过程中判断何时停止AOD任务。在AOD数据集上进行了充分的对比实验和烧蚀研究，证明了该方法比同类方法具有更好的性能，并且该训练算法比原始训练算法更有效。
This paper focuses on the problem of active object detection (AOD). AOD is important for service robots to complete tasks in the family environment, and leads robots to approach the target object by taking appropriate moving actions. Most of the current AOD methods are based on reinforcement learning with low training efficiency and testing accuracy. Therefore, an AOD model based on a deep Q-learning network (DQN) with a novel training algorithm is proposed in this paper. The DQN model is designed to fit the Q-values of various actions, and includes state space, feature extraction, and a multilayer perceptron. In contrast to existing research, a novel training algorithm based on memory is designed for the proposed DQN model to improve training efficiency and testing accuracy. In addition, a method of generating the end state is presented to judge when to stop the AOD task during the training process. Sufficient comparison experiments and ablation studies are performed based on an AOD dataset, proving that the presented method has better performance than the comparable methods and that the proposed training algorithm is more effective than the raw training algorithm.
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DOI：
10.1109/cvprw.2018.00277
发表时间：
2018-06
期刊：
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
影响因子：
--
作者：
Phil Ammirato;A. Berg;J. Kosecka
通讯作者：
Phil Ammirato;A. Berg;J. Kosecka
DOI：
10.1109/icra.2019.8793493
发表时间：
2018-05
期刊：
2019 International Conference on Robotics and Automation (ICRA)
影响因子：
--
作者：
Arsalan Mousavian;Alexander Toshev;Marek Fiser;J. Kosecka;James Davidson
通讯作者：
Arsalan Mousavian;Alexander Toshev;Marek Fiser;J. Kosecka;James Davidson
DOI：
10.1109/iros40897.2019.8967805
发表时间：
2019-11
期刊：
2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
影响因子：
--
作者：
Jan Fabian Schmid;M. Lauri;S. Frintrop
通讯作者：
Jan Fabian Schmid;M. Lauri;S. Frintrop
DOI：
10.1631/fitee.1800275
发表时间：
2019-08
影响因子：
3
作者：
Qi Wang;Zhen Fan;W. Sheng;Sen-lin Zhang;Mei-qin Liu
通讯作者：
Qi Wang;Zhen Fan;W. Sheng;Sen-lin Zhang;Mei-qin Liu
影响因子：
2.8
作者：
Dos Reis, Douglas Henke;Welfer, Daniel;Tello Gamarra, Daniel Fernando
通讯作者：
Tello Gamarra, Daniel Fernando