Planning and Action Control for Robots in Human Environments

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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.