Semantic Task Planning for Service Robots in Open Worlds

Abstract

This paper presents a planning system based on semantic reasoning for a general-purpose service robot, which is aimed at behaving more intelligently in domains that contain incomplete information, under-specified goals, and dynamic changes. First, Two kinds of data are generated by Natural Language Processing module from the speech: (i) action frames and their relationships; (ii) the modifier used to indicate some property or characteristic of a variable in the action frame. Next, the task’s goals are generated from these action frames and modifiers. These goals are represented as AI symbols, combining world state and domain knowledge, which are used to generate plans by an Answer Set Programming solver. Finally, the plan’s actions are executed one by one, and continuous sensing grounds useful information, which makes the robot use contingent knowledge to adapt to dynamic changes and faults. For each action in the plan, the planner gets its preconditions and effects from domain knowledge, so during the execution of the task, the environmental changes, especially those conflict with the actions, not only the action being performed but also the subsequent actions, can be detected and handled as early as possible. A series of case studies are used to evaluate the system and verify its ability to acquire knowledge through dialogue with users, solve problems with the acquired causal knowledge, and plan for complex tasks autonomously in the open world.

Logic programming for deliberative robotic task planning

“…Relevant applications of Clingo have been proposed by Pianpak et al (2019), where its variant Asprilo (Gebser et al 2018a) for multi-robot logistics and warehouse automation, is exploited; Opfer et al (2018), where Clingo system is integrated with the ALICA language for teamworking in domestic and general-purpose environment (Opfer et al 2019); Cui et al (2021) for general-purpose service robotics; Lu et al ( 2017), proposing a task planning framework for domestic service robots receiving information and goal description in natural language from humans, at the RoboCup@Home challenge; Ginesi et al (2020);Meli et al (2021b); Tagliabue et al (2022), where an integrated framework for interpretable surgical task-motion planning is proposed; Andres et al (2015), which shows the ROSoClingo package integrating Clingo in ROS. A recent application based on the DLV system can be found in Wang et al (2009), addressing the problem of mechanical assembly sequence in industry.…”

Reasoning with Scene Graphs for Robot Planning under Partial Observability

“…Algorithm iCORPP enables a robot to reason with contextual knowledge to compute parameters of a planning agent's reward and transition functions [22]. Others have used commonsense knowledge to guide a classical planner to reason and plan in open worlds [23], [24]. Compared to those methods, SARP uses contextual, object centric information, in the form of a graph, to estimate the current world state and guide robot planning under uncertainty.…”