A graph-rewriting approach to high-level task planning-an introduction

2002 ◽  
Author(s):  
N. Sarkar ◽  
M.S. Sarkar
Keyword(s):  
Task Planning ◽  
Graph Rewriting ◽  
High Level

scholarly journals The PETLON Algorithm to Plan Efficiently for Task-Level-Optimal Navigation

2020 ◽  
Vol 69 ◽  
pp. 471-500
Author(s):  
Shih-Yun Lo ◽  
Shiqi Zhang ◽  
Peter Stone
Keyword(s):  
Mobile Robot ◽  
Motion Planning ◽  
Mobile Robots ◽  
Large Scale ◽  
Accurate Estimate ◽  
Indoor Environments ◽  
Task Planning ◽  
Trade Offs ◽  
High Level ◽  
Task Level

Intelligent mobile robots have recently become able to operate autonomously in large-scale indoor environments for extended periods of time. In this process, mobile robots need the capabilities of both task and motion planning. Task planning in such environments involves sequencing the robot’s high-level goals and subgoals, and typically requires reasoning about the locations of people, rooms, and objects in the environment, and their interactions to achieve a goal. One of the prerequisites for optimal task planning that is often overlooked is having an accurate estimate of the actual distance (or time) a robot needs to navigate from one location to another. State-of-the-art motion planning algorithms, though often computationally complex, are designed exactly for this purpose of finding routes through constrained spaces. In this article, we focus on integrating task and motion planning (TMP) to achieve task-level-optimal planning for robot navigation while maintaining manageable computational efficiency. To this end, we introduce TMP algorithm PETLON (Planning Efficiently for Task-Level-Optimal Navigation), including two configurations with different trade-offs over computational expenses between task and motion planning, for everyday service tasks using a mobile robot. Experiments have been conducted both in simulation and on a mobile robot using object delivery tasks in an indoor office environment. The key observation from the results is that PETLON is more efficient than a baseline approach that pre-computes motion costs of all possible navigation actions, while still producing plans that are optimal at the task level. We provide results with two different task planning paradigms in the implementation of PETLON, and offer TMP practitioners guidelines for the selection of task planners from an engineering perspective.

scholarly journals RTPO: A Domain Knowledge Base for Robot Task Planning

Electronics ◽  
2019 ◽  
Vol 8 (10) ◽  
pp. 1105 ◽  
Author(s):  
Sun ◽  
Zhang ◽  
Chen
Keyword(s):  
Knowledge Base ◽  
Domain Knowledge ◽  
Task Planning ◽  
Specific Domain ◽  
Goal State ◽  
Ontology Language ◽  
Planning Algorithm ◽  
Domain Knowledge Base ◽  
Robot Task ◽  
High Level

Knowledge can enhance the intelligence of robots’ high-level decision-making. However, there is no specific domain knowledge base for robot task planning in this field. Aiming to represent the knowledge in robot task planning, the Robot Task Planning Ontology (RTPO) is first designed and implemented in this work, so that robots can understand and know how to carry out task planning to reach the goal state. In this paper, the RTPO is divided into three parts: task ontology, environment ontology, and robot ontology, followed by a detailed description of these three types of knowledge, respectively. The OWL (Web Ontology Language) is adopted to represent the knowledge in robot task planning. Then, the paper proposes a method to evaluate the scalability and responsiveness of RTPO. Finally, the corresponding task planning algorithm is designed based on RTPO, and then the paper conducts experiments on the basis of the real robot TurtleBot3 to verify the usability of RTPO. The experimental results demonstrate that RTPO has good performance in scalability and responsiveness, and the robot can achieve given high-level tasks based on RTPO.

Towards life-long adaptive agents: using metareasoning for combining knowledge-based planning with situated learning

2018 ◽  
Vol 33 ◽  
Author(s):  
Priyam Parashar ◽  
Ashok K. Goel ◽  
Bradley Sheneman ◽  
Henrik I. Christensen
Keyword(s):  
Situated Learning ◽  
Intelligent Agents ◽  
Physical World ◽  
Task Planning ◽  
Adaptive Agents ◽  
Hierarchical Task Network ◽  
Knowledge Based ◽  
Knowledge Based Planning ◽  
Occupancy Grids ◽  
High Level

AbstractWe consider task planning for long-living intelligent agents situated in dynamic environments. Specifically, we address the problem of incomplete knowledge of the world due to the addition of new objects with unknown action models. We propose a multilayered agent architecture that uses meta-reasoning to control hierarchical task planning and situated learning, monitor expectations generated by a plan against world observations, forms goals and rewards for the situated reinforcement learner, and learns the missing planning knowledge relevant to the new objects. We use occupancy grids as a low-level representation for the high-level expectations to capture changes in the physical world due to the additional objects, and provide a similarity method for detecting discrepancies between the expectations and the observations at run-time; the meta-reasoner uses these discrepancies to formulate goals and rewards for the learner, and the learned policies are added to the hierarchical task network plan library for future re-use. We describe our experiments in the Minecraft and Gazebo microworlds to demonstrate the efficacy of the architecture and the technique for learning. We test our approach against an ablated reinforcement learning (RL) version, and our results indicate this form of expectation enhances the learning curve for RL while being more generic than propositional representations.

Integration of Symbolic Task Planning into Operations within an Unstructured Environment

2012 ◽  
Vol 2 (3) ◽  
pp. 38-57 ◽  
Author(s):  
Renxi Qiu ◽  
Alexandre Noyvirt ◽  
Ze Ji ◽  
Anthony Soroka ◽  
Dayou Li ◽  
...  
Keyword(s):  
Real Time ◽  
Time Perception ◽  
Domain Knowledge ◽  
Task Planning ◽  
Task Execution ◽  
Unstructured Environments ◽  
Planning Approach ◽  
The World ◽  
Unstructured Environment ◽  
High Level

To ensure a robot capable of robust task execution in unstructured environments, task planners need to have a high-level understanding of the nature of the world, reasoning for deliberate actions, and reacting to environment changes. Proposed is a practical task planning approach that seamlessly integrating deeper domain knowledge, real time perception and symbolic planning for robot operation. A higher degree of autonomy under unstructured environment will be endowed to the robot with the proposed approach.

scholarly journals Integrating Answer Set Programming with Semantic Dictionaries for Robot Task Planning

2017 ◽  
Author(s):  
Dongcai Lu ◽  
Yi Zhou ◽  
Feng Wu ◽  
Zhao Zhang ◽  
Xiaoping Chen
Keyword(s):  
Answer Set Programming ◽  
Planning System ◽  
Service Robot ◽  
Task Planning ◽  
Planning Framework ◽  
Representation Language ◽  
Robot Task ◽  
User Instructions ◽  
High Level ◽  
Answer Set

In this paper, we propose a novel integrated task planning system for service robot in domestic domains. Given open-ended high-level user instructions in natural language, robots need to generate a plan, i.e., a sequence of low-level executable actions, to complete the required tasks. To address this, we exploit the knowledge on semantic roles of common verbs defined in semantic dictionaries such as FrameNet and integrate it with Answer Set Programming --- a task planning framework with both representation language and solvers. In the experiments, we evaluated our approach using common benchmarks on service tasks and showed that it can successfully handle much more tasks than the state-of-the-art solution. Notably, we deployed the proposed planning system on our service robot for the annual RoboCup@Home competitions and achieved very encouraging results.

Manipulation Task Planning with Constrained Kinematic Controller

2020 ◽  
Author(s):  
Marcos S. Pereira ◽  
Bruno V. Adorno
Keyword(s):  
Motion Planning ◽  
Geometric Constraints ◽  
Task Planning ◽  
Motion Controller ◽  
Planning Time ◽  
Low Level ◽  
Pick And Place ◽  
Manipulation Task ◽  
The One ◽  
High Level

This paper addresses the integration of task planning and motion control in robotic manipulation, where automatically generated feasible manipulation sequences are executed by a controller that explicitly accounts for the task geometric constraints. To cope with the high dimensionality of the manipulation problem and the complexity of specifying the tasks, we use a multi-layered framework for task and motion planning adapted from the literature. The adapted framework consists of a high-level planner, which generates task plans for linear temporal logic specifications, and a low-level motion controller, based on constrained optimization, that allows to define regions of interest instead of exact locations while being reactive to changes in theworkspace. Thus, there is no low-level motion planning time added to the total planning time. Moreover, since there is no replanning phase due to motion planner failures, the robot actions are generated only once for each task because the search for a plan occurs on a static graph. We evaluated this approach with two pick-and-place tasks with similar complexity to the original framework and showed that the number of plan nodes generated is smaller than the one in the original framework, which implies less total planning time.

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