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Service skill improvement for home robots: Autonomous generation of action sequence based on reinforcement learning
Knowledge-Based Systems ( IF 8.8 ) Pub Date : 2020-11-20 , DOI: 10.1016/j.knosys.2020.106605
Mengyang Zhang , Guohui Tian , Ying Zhang , Peng Duan

It still remains a challenge for robots to obtain knowledge automatically for performing home services. In the human learning process, natural languages act as an outline in guiding human beings complete tasks. From this point, a conditional generation method transforming textual manipulation instructions into action sequences is proposed, to provide home robots with knowledge automatically and improve the service skills finally. Due to the limited learning ability of the generation model on understanding complex semantic information, we present a two-phase conditional generation strategy in which the action space is reduced at the syntax level before generating action sequences semantically. For representing action sequences effectively, functional labels (FLs) are designed according to the requirements of performing home services, to identify six relationships about objects and actions. In action sequence generation, reinforcement learning is employed to guide the action sequence generation by introducing hierarchical rewards related to a priori knowledge, semantic similarity, and action logic. Based on statistic learning, a priori knowledge is constructed by modeling the relationship about object co-occurrence, action collaboration, and action–object correlation. The semantic similarity with Semantic Role Labeling enables the similarity evaluation between textual sentences (inputs) and produced sequences (outputs). And action logic, represented by the verb sequence in instructions, guides the production of action sequences logically. Experimental results demonstrate that the proposed method can produce competitive action sequences from textual instructions, and produced action sequences can be applied to robot for performing services.



中文翻译:

改善家用机器人的服务技能:基于强化学习的动作序列自主生成

对于机器人来说,自动获取知识以执行家庭服务仍然是一个挑战。在人类的学习过程中,自然语言是指导人类完成任务的大纲。从这一点出发,提出了一种将文本操作指令转换为动作序列的条件生成方法,以自动为家用机器人提供知识,最终提高服务技能。由于生成模型在理解复杂语义信息方面的学习能力有限,我们提出了一种两阶段条件生成策略,其中在语义生成动作序列之前,在语法级别上减少了动作空间。为了有效地表示动作序列,根据执行家庭服务的要求设计了功能标签(FL),确定关于对象和动作的六个关系。在动作序列生成中,通过引入与先验知识,语义相似性和动作逻辑相关的等级奖励,强化学习被用来指导动作序列的生成。在统计学习的基础上,通过对对象共现,动作协作以及动作-对象相关性之间的关系进行建模来构造先验知识。带有语义角色标记的语义相似性使文本句子(输入)和产生的序列(输出)之间的相似性评估成为可能。动作逻辑由指令中的动词序列表示,从逻辑上指导动作序列的产生。实验结果表明,该方法可以根据文字说明产生竞争性动作序列,

更新日期:2020-11-25
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