服务机器人中的慎思和概念推理
Deliberative and Conceptual Inference in Service Robots
摘要：
服务机器人需要推理来支持人们的日常生活。推理是一种昂贵的资源，每当机器人的期望与世界的情况不匹配并且任务的执行被打破时，应按需使用;在这种情况下，机器人必须执行常识日常生活推理循环，包括诊断发生了什么，决定对此做什么，以及归纳和执行计划，以这种行为重复，直到可以恢复服务任务。在这里，我们研究了两种策略来实现这个循环：（1）一个管道线策略，涉及绑架，决策和规划，我们称之为慎思推理和（2）使用存储在机器人的知识库中的知识和偏好，我们称之为概念推理。前者涉及一个明确的定义，通过启发式搜索探索的问题空间，后者是基于概念知识，包括人类用户的喜好，其表示需要一个非单调的知识为基础的系统。我们比较了这两种方法的优势和局限性。我们还描述了一个服务机器人的概念模型和架构，能够支持日常生活的推理周期在执行机器人服务任务。该模型集中在机器人的通信和任务结构的声明性规范和解释。我们还展示了这个框架在全自主机器人Golem-III中的实现。该框架说明了两个示范方案。
英文摘要：
Service robots need to reason to support people in daily life situations. Reasoning is an expensive resource that should be used on demand whenever the expectations of the robot do not match the situation of the world and the execution of the task is broken down; in such scenarios the robot must perform the common sense daily life inference cycle consisting on diagnosing what happened, deciding what to do about it, and inducing and executing a plan, recurring in such behavior until the service task can be resumed. Here we examine two strategies to implement this cycle: (1) a pipe-line strategy involving abduction, decision-making and planning, which we call deliberative inference and (2) the use of the knowledge and preferences stored in the robot's knowledge-base, which we call conceptual inference. The former involves an explicit definition of a problem space that is explored through heuristic search, and the latter is based on conceptual knowledge including the human user preferences, and its representation requires a non-monotonic knowledge-based system. We compare the strengths and limitations of both approaches. We also describe a service robot conceptual model and architecture capable of supporting the daily life inference cycle during the execution of a robotics service task. The model is centered in the declarative specification and interpretation of robot's communication and task structure. We also show the implementation of this framework in the fully autonomous robot Golem-III. The framework is illustrated with two demonstration scenarios.
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