Planning and Action Control for Robots in Human Environments

人类环境中机器人的规划和动作控制

• 批准号: 214256985
• 负责人: Professor Dr. Wolfram Burgard
• 金额: --
• 依托单位: Arbeitsgruppe Grundlagen der Künstlichen Intelligenz
• 依托单位国家: 德国
• 项目类别: Research Units
• 财政年份: 2012
• 资助国家: 德国
• 起止时间: 2011-12-31 至 2019-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/214256985?language=en
• 关键词: Planning; Action; Control; Robots; Human

While the first phase of the project focused on active perception and reasoning under uncertainty for mobile manipulation tasks, the second phase will primarily address aspects of hybrid reasoning in the context of robots operating alongside humans in domestic environments. In particular, we will develop methods that let a robot assist humans in an unobtrusive way and be highly adaptable during task planning and execution, taking into account that some actions need to be learned on the fly and beliefs about the world may be incorrect. This implies among other things that acquiring user preferences will play a key role so that the robot does not have to rely on explicit instructions by the user. Furthermore, task execution and planning must account for task interruptions and possible reorderings. As a special case, we will also consider the case that the robot has to learn a new skill on the fly such as grasping an object of a new type. Such flexibility has to be supported by a new kind of planning component that can react to changes quickly. For example, the already explored search spaces could be reused when replanning and the geometrical part of the planning problem could be made more transparent so that it becomes possible to apply standard heuristic search techniques. All this will be based on using Golog as the execution engine, which has to be extended to account for interruptions, and an accompanying database for recording acquired observations and beliefs. Here we will, in particular, address the problem of revising incorrect beliefs. In the end, the approach will allow for implementing a robotic system that will be, for example, able to assist a host at a party by helping guests and serving snacks and drinks.

虽然该项目的第一阶段侧重于移动操纵任务的不确定性下的主动感知和推理，但第二阶段将主要解决在家庭环境中与人类一起操作的机器人的混合推理的各个方面。特别是，我们将开发一些方法，让机器人以一种不引人注目的方式帮助人类，并在任务规划和执行过程中具有高度适应性，考虑到一些动作需要在飞行中学习，对世界的信念可能是不正确的。这意味着，获取用户偏好将起到关键作用，这样机器人就不必依赖用户的明确指令。此外，任务执行和计划必须考虑到任务中断和可能的重新排序。作为特例，我们还将考虑机器人必须在飞行中学习新技能的情况，例如抓取新类型的对象。这种灵活性必须得到一种新的规划组件的支持，该组件可以对变化做出快速反应。例如，在重新规划时，可以重复使用已经探索的搜索空间，并且可以使规划问题的几何部分更加透明，从而可以应用标准的启发式搜索技术。所有这一切都将基于使用Golog作为执行引擎，它必须进行扩展以解决中断问题，以及一个附带的数据库，用于记录获得的观察和信念。在这里，我们将特别讨论修正不正确信念的问题。最终，这种方法将允许实施一个机器人系统，例如，能够通过帮助客人以及提供零食和饮料来帮助派对上的主人。