KNOWROB &amp;#x2014; knowledge processing for autonomous personal robots

Tenorth, Moritz; Beetz, Michael

doi:10.1109/iros.2009.5354602

Cited by 251 publications

(210 citation statements)

References 8 publications

Supporting

Mentioning

210

Contrasting

Order By: Relevance

“…Future work will integrate function calls that provide in better cost estimates, either by learning [27] or physics simulations [28]. Currently also the knowledge processing framework KnowRob [29] is being integrated, to enable more extensive reasoning capabilities. On a short term we want to replace the pool of PDDL operators and methods by the globally accesible RoboEarth Action Recipe database, allowing to share and reuse actions with other systems through the common OWL-DL action representation.…”

Section: Discussion and Future Workmentioning

confidence: 99%

“…As part of the EU funded RoboEarth and RoboHow 1 projects a first implementation is described in [10], where the Semantic Robot Description Language (SRDL) [11] is used to match robot capabilities against the task related components. Although this capability matching enables the filtration of plans that cannot be executed by platforms missing the required capabilities, it does not project the state of the world into the future, therefore not ensuring that a certain robot feature remains available throughout the execution phase of the plan (e.g.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Integrating planning and execution for ROS enabled service robots using hierarchical action representations

Janssen

Meijl

Marco

et al. 2013

2013 16th International Conference on Advanced Robotics (ICAR)

View full text Add to dashboard Cite

Abstract-The aim of the RoboEarth project is to develop a globally accessible database, that enables service robots to share reusable information relevant to the execution of their daily tasks. Examples of this information are the hierarchical task descriptions, or action recipes, that represent typical household tasks as symbolic action sequences. By annotating these static action representations with hierarchical planner predicates, they can be interpreted by the Hierarchical Task Network planner SHOP2 to compose more flexible, optimized robot plans, based on the actual state of the environment and the available capabilities of the robot. To subsequently execute the composed plans in a typical household environment, the CRAM executive toolbox is adopted, allowing a tight integration between plan execution and run-time knowledge inference. This paper presents the integration of these two components into one cohesive planning and execution framework, tailored for the safe execution of abstract tasks in a challenging household environment. The resulting framework is implemented on the AMIGO service robot and a basic experiment is conducted to demonstrate the frameworks integral functionality.

show abstract

Section: Discussion and Future Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Integrating planning and execution for ROS enabled service robots using hierarchical action representations

Janssen

Meijl

Marco

et al. 2013

2013 16th International Conference on Advanced Robotics (ICAR)

View full text Add to dashboard Cite

show abstract

“…There has been a lot of work in the field of intelligent systems on developing formalisms for learning and representing actions, ranging from task-space representations [1] to high-level symbolic description of actions and their effects on objects [2]. Many approaches work directly on raw video streams, which is a challenging problem due to high variance in the video emanating from e.g.…”

Section: Related Workmentioning

confidence: 99%

Comparing Hidden Markov Models and Long Short Term Memory Neural Networks for Learning Action Representations

Panzner

Cimiano

2016

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Abstract. In this paper we are concerned with learning models of actions and compare a purely generative model based on Hidden Markov Models to a discriminatively trained recurrent LSTM network in terms of their properties and their suitability to learn and represent models of actions. Specifically we compare the performance of the two models regarding the overall classification accuracy, the amount of training sequences required and how early in the progression of a sequence they are able to correctly classify the corresponding sequence. We show that, despite the current trend towards (deep) neural networks, traditional graphical model approaches are still beneficial under conditions where only few data points or limited computing power is available.

show abstract

“…The knowledge processing framework KnowRob [20] is used as interlingua to exchange knowledge between different components of the RoboEarth system. When the execution engine queries for a recipe, KnowRob searches the RoboEarth database, downloads recipes, checks for missing components (such as environment maps or object models that are required for the task), and triggers their download.…”

Section: Representations For Exchanging Knowledgementioning

confidence: 99%

“…Recipes are stored in description logic in a language built upon the W3C-standardized Web Ontology Language [22] and the KnowRob knowledge base [20]. The language itself is described in [21].…”

Section: Action Recipesmentioning

confidence: 99%

RoboEarth Action Recipe Execution

Marco¹,

Tenorth²,

Häußermann³

et al. 2013

Frontiers of Intelligent Autonomous Systems

View full text Add to dashboard Cite

The ability of reusing existing task execution plans is an important step towards autonomous behavior. Today, the reuse of sophisticated services allowing robots to act autonomous is usually limited to identical robot platforms and to very similar application scenarios. The approach presented in this paper proposes a way to mitigate this limitation by storing and reusing task plans on a global accessible database. We describe the task execution engine for the RoboEarth project [26] to demonstrate its ability to execute tasks in a flexible and reliable way.

show abstract

KNOWROB — knowledge processing for autonomous personal robots

Cited by 251 publications

References 8 publications

Integrating planning and execution for ROS enabled service robots using hierarchical action representations

Integrating planning and execution for ROS enabled service robots using hierarchical action representations

Comparing Hidden Markov Models and Long Short Term Memory Neural Networks for Learning Action Representations

RoboEarth Action Recipe Execution

Contact Info

Product

Resources

About