Planning and Action Control for Robots in Human Environments

虽然该项目的第一阶段侧重于移动的操作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机器人在家庭环境中与人类一起操作的混合推理方面。特别是，我们将开发方法，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的适应性，同时考虑到一些行动需要在飞行中学习，并且对世界的信念可能是不正确的。这意味着获取用户偏好将发挥关键作用，因此机器人不必依赖用户的明确指令。此外，任务执行和规划必须考虑任务中断和可能的重新排序。作为特殊情况，我们还将考虑机器人必须在飞行中学习新技能的情况，例如抓取新类型的物体。这种灵活性必须得到一种新的规划组成部分的支持，这种规划组成部分可以对变化迅速作出反应。例如，当重新规划时，可以重新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，使得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，它必须扩展以考虑中断，以及用于记录所获得的观察和信念的附带数据库。在这里，我们将特别讨论修正错误信念的问题。最后，这种方法将允许实现一个机器人系统，例如，能够通过帮助客人和提供小吃和饮料来帮助聚会上的主人。

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.