Open robotics research using web-based knowledge services

Beetz, Michael; Bebler, Daniel; Winkler, Jan; Worch, Jan-Hendrik; Balint-Benczedi, Ferenc; Bartels, G.; Billard, Aude; Bozcuoglu, Asil Kaan; Fang, Zhou; Figueroa, Nadia; Haidu, Andrei; Langer, Hagen; Maldonado, Alexis; Ureche, Ana Lucia Pais; Tenorth, Moritz; Wiedemeyer, Thiemo

doi:10.1109/icra.2016.7487749

Cited by 14 publications

(12 citation statements)

References 7 publications

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“…Alternative studies do exist that emphasize data collection from robotic experiments in both industry-oriented (Björkelund et al, 2011; Persson et al, 2010) and service robotics domains (Beetz et al, 2016; Ovchinnikova et al, 2015; Riazuelo et al, 2015; Tenorth and Beetz, 2013; Tenorth et al, 2013; Winkler et al, 2014). In the following, we will discuss how the mentioned approaches relate to our study.…”

Section: Discussionmentioning

confidence: 99%

“…Recently, research has also targeted the reduction of robot programming and training efforts. Here, usage of advanced visual interfaces (Huang et al, 2016; Schlette et al, 2014), also paired with touch or gestures (Profanter et al, 2015), natural language instruction (Bollini et al, 2013; Misra et al, 2016; Stenmark and Nugues, 2013; Tellex et al, 2011), knowledge-based methods (Beetz et al, 2016; Tenorth and Beetz, 2013), and advanced grasp and motion planning (Alterovitz et al, 2016; Bohg et al, 2014), allow the robot to behave in new environments.…”

Section: Introductionmentioning

confidence: 99%

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Cut & recombine: reuse of robot action components based on simple language instructions

Tamošiūnaitė

Aein

Braun

et al. 2019

The International Journal of Robotics Research

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Human beings can generalize from one action to similar ones. Robots cannot do this and progress concerning information transfer between robotic actions is slow. We have designed a system that performs action generalization for manipulation actions in different scenarios. It relies on an action representation for which we perform code-snippet replacement, combining information from different actions to form new ones. The system interprets human instructions via a parser using simplified language. It uses action and object names to index action data tables (ADTs), where execution-relevant information is stored. We have created an ADT database from three different sources (KUKA LWR, UR5, and simulation) and show how a new ADT is generated by cutting and recombining data from existing ADTs. To achieve this, a small set of action templates is used. After parsing a new instruction, index-based searching finds similar ADTs in the database. Then the action template of the new action is matched against the information in the similar ADTs. Code snippets are extracted and ranked according to matching quality. The new ADT is created by concatenating code snippets from best matches. For execution, only coordinate transforms are needed to account for the poses of the objects in the new scene. The system was evaluated, without additional error correction, using 45 unknown objects in 81 new action executions, with 80% success. We then extended the method including more detailed shape information, which further reduced errors. This demonstrates that cut & recombine is a viable approach for action generalization in service robotic applications.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Cut & recombine: reuse of robot action components based on simple language instructions

Tamošiūnaitė

Aein

Braun

et al. 2019

The International Journal of Robotics Research

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“…KnowRob has also been used in several research initiatives including the European projects RoboHow (Beetz, Beßler, Winkler, et al 2016), RoboEarth (Waibel et al 2011), SAPHARI (Beetz, Bartels, et al 2015), and SHERPA (Marconi et al 2012). RoboEarth, for example, is a pioneer work to consider exchanging knowledge between robots using the World Wide Web, OWL, and Linked Data principles.…”

Section: Knowrobmentioning

confidence: 99%

A review and comparison of ontology-based approaches to robot autonomy

Olivares-Alarcos

Beßler

Khamis

et al. 2019

The Knowledge Engineering Review

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Within the next decades, robots will need to be able to execute a large variety of tasks autonomously in a large variety of environments. To relax the resulting programming effort, a knowledge-enabled approach to robot programming can be adopted to organize information in re-usable knowledge pieces. However, for the ease of reuse, there needs to be an agreement on the meaning of terms. A common approach is to represent these terms using ontology languages that conceptualize the respective domain. In this work, we will review projects that use ontologies to support robot autonomy. We will systematically search for projects that fulfill a set of inclusion criteria and compare them with each other with respect to the scope of their ontology, what types of cognitive capabilities are supported by the use of ontologies, and which is their application domain.

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“…In the example at hand, this allows researchers to query multiple episodic memories of compliant manipulation tasks at once, by formulating relatively simple queries based on symbols related to physical interaction, such as contact or collision. Ideally, these narratives are generated during runtime by the control program of the robot [48]. This is possible if all states and transitions are fully observable as it is common for state machines and symbolic planners.…”

Section: Semantic Analysis With Openeasementioning

confidence: 99%

Cognition-enabled robotic wiping: Representation, planning, execution, and interpretation

Leidner

Bartels

Bejjani

et al. 2019

Robotics and Autonomous Systems

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Advanced cognitive capabilities enable humans to solve even complex tasks by representing and processing internal models of manipulation actions and their effects. Consequently, humans are able to plan the effect of their motions before execution and validate the performance afterwards. In this work, we derive an analog approach for robotic wiping actions which are fundamental for some of the most frequent household chores including vacuuming the floor, sweeping dust, and cleaning windows. We describe wiping actions and their effects based on a qualitative particle distribution model. This representation enables a robot to plan goal-oriented wiping motions for the prototypical wiping actions of absorbing, collecting and skimming. The particle representation is utilized to simulate the task outcome before execution and infer the real performance afterwards based on haptic perception. This way, the robot is able to estimate the task performance and schedule additional motions if necessary. We evaluate our methods in simulated scenarios, as well as in real experiments with the humanoid service robot Rollin' Justin.

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Open robotics research using web-based knowledge services

Cited by 14 publications

References 7 publications

Cut & recombine: reuse of robot action components based on simple language instructions

Cut & recombine: reuse of robot action components based on simple language instructions

A review and comparison of ontology-based approaches to robot autonomy

Cognition-enabled robotic wiping: Representation, planning, execution, and interpretation

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