The emergence and popularization of medical robots bring great convenience to doctors in treating patients. The core of medical robots is the interaction and cooperation between doctors and robots, so it is crucial to design a simple and stable human-robots interaction system for medical robots. Language is the most convenient way for people to communicate with each other, so in this paper, a DQN agent based on long-short term memory (LSTM) and attention mechanism is proposed to enable the robots to extract executable action sequences from doctors’ natural language instructions. For this, our agent should be able to complete two related tasks: 1) extracting action names from instructions. 2) extracting action arguments according to the extracted action names. We evaluate our agent on three datasets composed of texts with an average length of 49.95, 209.34, 417.17 words respectively. The results show that our agent can perform better than similar agents. And our agent has a better ability to handle long texts than previous works.

中文翻译：

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基于DQN的医疗服务机器人从自然语言指令中提取可执行动作序列

医疗机器人的出现和普及为医生治疗患者带来了极大的便利。医疗机器人的核心是医生与机器人之间的交互与合作，因此设计一种简单稳定的医疗机器人人机交互系统至关重要。语言是人们之间最方便的交流方式，因此，本文提出了一种基于长短期记忆（LSTM）和注意力机制的DQN代理，以使机器人能够从医生的自然行为中提取可执行的动作序列。语言说明。为此，我们的代理应该能够完成两项相关任务：1）从指令中提取动作名称。2）根据提取的动作名称提取动作自变量。我们在三个平均长度为49.95的文本组成的数据集上评估我们的代理，分别为209.34和417.17个字。结果表明，我们的代理人可以比同类代理人表现更好。而且我们的代理人比以前的作品具有更好的处理长文本的能力。