High-level robot task specification

2003 ◽  
Author(s):  
N. Abe ◽  
S. Sako ◽  
S. Tsuji
Keyword(s):  
Robot Task ◽  
Task Specification ◽  
High Level

scholarly journals Building the Foundation of Robot Explanation Generation Using Behavior Trees

2021 ◽  
Vol 10 (3) ◽  
pp. 1-31
Author(s):  
Zhao Han ◽  
Daniel Giger ◽  
Jordan Allspaw ◽  
Michael S. Lee ◽  
Henny Admoni ◽  
...  
Keyword(s):  
Autonomous Robots ◽  
Free Form ◽  
Static Structure ◽  
Simulation Code ◽  
Causal Information ◽  
Robot Behavior ◽  
Behavior Trees ◽  
Robot Task ◽  
Task Specification ◽  
Explanation Generation

As autonomous robots continue to be deployed near people, robots need to be able to explain their actions. In this article, we focus on organizing and representing complex tasks in a way that makes them readily explainable. Many actions consist of sub-actions, each of which may have several sub-actions of their own, and the robot must be able to represent these complex actions before it can explain them. To generate explanations for robot behavior, we propose using Behavior Trees (BTs), which are a powerful and rich tool for robot task specification and execution. However, for BTs to be used for robot explanations, their free-form, static structure must be adapted. In this work, we add structure to previously free-form BTs by framing them as a set of semantic sets {goal, subgoals, steps, actions} and subsequently build explanation generation algorithms that answer questions seeking causal information about robot behavior. We make BTs less static with an algorithm that inserts a subgoal that satisfies all dependencies. We evaluate our BTs for robot explanation generation in two domains: a kitting task to assemble a gearbox, and a taxi simulation. Code for the behavior trees (in XML) and all the algorithms is available at github.com/uml-robotics/robot-explanation-BTs.

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.

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.

Automatic robot program synthesis for assembly

Robotica ◽  
1992 ◽  
Vol 10 (2) ◽  
pp. 113-123 ◽  
Author(s):  
H. A. ElMaraghy ◽  
J. M. Rondeau
Keyword(s):  
Error Detection ◽  
Automatic Generation ◽  
Planning System ◽  
Problem Solver ◽  
Task Definition ◽  
Robot Trajectory ◽  
Planning Framework ◽  
Robot Task ◽  
Definition Of ◽  
High Level

SUMMARYThis paper describes a revised version of ROBOPLAN, a goal-oriented robot task planning system for automatic generation, decomposition and execution of high-level robot plans for assembly. It emphasizes its new features, i.e., modularity, formal definition of the task, robust plan synthesis, and execution of each assembly step. A task definition language allows a formal description of the robot universe and the assembly task to be input to ROBOPLAN. The expert task planner is a non-linear backward chaining problem solver, using a goal driven depth-first strategy. The implemented search strategy has been tested in the assembly domain, but it could be used in other domains where planning is needed. The motion planner provides a non-optimal, safe robot trajectory; collision free path planning has not been included yet. A robot executable code is generated for each assembly step and monitored in real time. The error detection and recovery capability of the system is rather limited at present, since no sensors are used. The initial implementation of the system has been tested and evaluated on the assembly of a DC motor. The potential of extending this planning framework to other applications is also discussed.

scholarly journals An integrated system for perception-driven autonomy with modular robots

2018 ◽  
Vol 3 (23) ◽  
pp. eaat4983 ◽  
Author(s):  
Jonathan Daudelin ◽  
Gangyuan Jing ◽  
Tarik Tosun ◽  
Mark Yim ◽  
Hadas Kress-Gazit ◽  
...  
Keyword(s):  
A Priori ◽  
Integrated System ◽  
Modular Robots ◽  
Modular Robot ◽  
Robot System ◽  
Unknown Environment ◽  
Unknown Environments ◽  
Task Specification ◽  
Mechanical Elements ◽  
High Level

The theoretical ability of modular robots to reconfigure in response to complex tasks in a priori unknown environments has frequently been cited as an advantage and remains a major motivator for work in the field. We present a modular robot system capable of autonomously completing high-level tasks by reactively reconfiguring to meet the needs of a perceived, a priori unknown environment. The system integrates perception, high-level planning, and modular hardware and is validated in three hardware demonstrations. Given a high-level task specification, a modular robot autonomously explores an unknown environment, decides when and how to reconfigure, and manipulates objects to complete its task. The system architecture balances distributed mechanical elements with centralized perception, planning, and control. By providing an example of how a modular robot system can be designed to leverage reactive reconfigurability in unknown environments, we have begun to lay the groundwork for modular self-reconfigurable robots to address tasks in the real world.

scholarly journals Adversarial Actor-Critic Method for Task and Motion Planning Problems Using Planning Experience

2019 ◽  
Vol 33 ◽  
pp. 8017-8024 ◽  
Author(s):  
Beomjoon Kim ◽  
Leslie Pack Kaelbling ◽  
Tomás Lozano-Pérez
Keyword(s):  
Motion Planning ◽  
Vector Representation ◽  
Search Trees ◽  
Training Set ◽  
Fixed Length ◽  
Different Shapes ◽  
Robot Task ◽  
High Level ◽  
Robot Configurations ◽  
Planning Problems

We propose an actor-critic algorithm that uses past planning experience to improve the efficiency of solving robot task-and-motion planning (TAMP) problems. TAMP planners search for goal-achieving sequences of high-level operator instances specified by both discrete and continuous parameters. Our algorithm learns a policy for selecting the continuous parameters during search, using a small training set generated from the search trees of previously solved instances. We also introduce a novel fixed-length vector representation for world states with varying numbers of objects with different shapes, based on a set of key robot configurations. We demonstrate experimentally that our method learns more efficiently from less data than standard reinforcementlearning approaches and that using a learned policy to guide a planner results in the improvement of planning efficiency.

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