# Semantic Task Planning for Service Robots in Open Worlds
## 1 Introduction  
Research on service robots has received increasing attention in recent years [1, 2, 3]. In most scenarios, like nursing homes and offices, humans hope that robots can help them do many tasks, which include taking orders and serving drinks, welcoming and guiding guests, or just cleaning up. To achieve that goal, a service robot requires human-like information processing and the underlying mechanisms for dealing with the real world, especially the ability to communicate with humans, acquire the knowledge to complete tasks and adapt to the dynamic environment. For most users, speech is preferable to other communication methods with a general-purpose service robot in most scenarios. Speech is also the primary communication method in General-Purpose Service Robot (GPSR) test defined by RoboCup@Home [[https://athome.robocup.org](https://athome.robocup.org)]([https://athome.robocup.org](https://athome.robocup.org)) (accessed on 12 February 2021). The user verbally assigns a complicated task to the robot that may require a set of skills. The robot needs to perform the task, report any problems, adapt to unexpected changes, and find alternative solutions with brief knowledge about the domain. Automated planning, which makes decisions about how to act in the world, requires symbolic representations of the robot's environment and the actions the robot can perform, has been widely used for task planning and control in many service robot applications. In the open world, there are two main challenges for task planning: (1) the robot's perception of the world is often incomplete, a command may refer to an object that is not in its knowledge base, lack of information will fail to generate a plan; (2) changes in the dynamic environment may not be expected by the robot, which will cause the planned action to fail. This paper addresses these issues by developing a semantic task planning system, which combines natural language understanding, task-oriented knowledge acquisition, and semantic-based automated task planning. Research in knowledge representation (KR) and logical reasoning has provided sophisticated algorithms [4; 5; 6], which have been used on service robots for supporting task planning and execution. [PERSON] et al. [7] attempt to combine descriptions of robot tasks using natural language together with their realizations using robot hardware to representing knowledge about industrial processes. The Kejia robot [8] represents domain knowledge learned through natural language processing and leverages a symbolic planner for problem-solving and planning to provide high-level functions [9; 10]. The system has been extended to acquire task-oriented knowledge by interacting with the user and sensing the environment [11]. By interacting and sensing and grounding useful sensing information, the robot can work with incomplete information and unexpected changes. [PERSON] et al. [12] present a high-level configuration and task assignment software package that distributes algorithms on a swarm of robots, which allows them to operate in a swarm fashion. [PERSON] et al. [3] use a conceptual-dependency [13] interpreter extracts semantic role structures from the input sentence and planning with the open-source expert system CLIPS [14]. [PERSON] et al. [15] adopt Soar cognitive architecture [16] to support understanding and executing human-specified commands. In [17], the robot's knowledge for safety is enhanced by using data coming from sensors with knowledge from reasoning. Respecting safety requirements, [18] aims to redesign existing industrial robotic cells for executing a number of collaborative actions based on a knowledge base. In this work, the robot maintains a certain distance from obstacles or people to ensure safety. Moreover, voice is also used to remind users to pay attention to safety. Similar to these works, our system combines a KR system and an ASP planner for high-level planning. Planning approaches that work in open-world scenarios often need to find a way to close the world.