A generic robot database and its application in fault analysis and performance evaluation

Niemueller, Tim; Lakemeyer, Gerhard; Srinivasa, Siddhartha S.

doi:10.1109/iros.2012.6385940

Cited by 36 publications

(24 citation statements)

References 11 publications

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“…In fact, Niemueller et al [23] have shown that it is feasible to apply a document-oriented database like MongoDB, even for logging raw sensor data and analyzing robots' behavior in retrospect. Also knowledge-enabled and ontologybased approaches such as KnowRob [5], Robo Brain [6] or the OpenRobot Ontology (ORO) [7] rely on knowledge bases to store and query specifications of robots, their capabilities, tasks and environments.…”

Section: Related Work and Discussionmentioning

confidence: 99%

Graph-based software knowledge: Storage and semantic querying of domain models for run-time adaptation

Hochgeschwender

Schneider

Voos

et al. 2016

2016 IEEE International Conference on Simulation, Modeling, and Programming for Autonomous Robots (SIMPAR)

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Abstract-Software development for robots is a knowledgeintensive exercise. To capture this knowledge explicitly and formally in the form of various domain models, roboticists have recently employed model-driven engineering (MDE) approaches. However, these models are merely seen as a way to support humans during the robot's software design process. We argue that the robots themselves should be first-class consumers of this knowledge to autonomously adapt their software to the various and changing run-time requirements induced, for instance, by the robot's tasks or environment.Motivated by knowledge-enabled approaches, we address this problem by employing a graph-based knowledge representation that allows us not only to persistently store domain models, but also to formulate powerful queries for the sake of run time adaptation. We have evaluated our approach in an integrated, real-world system using the neo4j graph database and we report some lessons learned. Further, we show that the graph database imposes only little overhead on the system's overall performance.

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Section: Related Work and Discussionmentioning

confidence: 99%

Graph-based software knowledge: Storage and semantic querying of domain models for run-time adaptation

Hochgeschwender

Schneider

Voos

et al. 2016

2016 IEEE International Conference on Simulation, Modeling, and Programming for Autonomous Robots (SIMPAR)

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“…Work that also relies on automatic data storage from previous successful experiences was proposed by Niemueller et al [22]. Data is queried automatically during the execution of skills and stored to a database.…”

Section: Related Workmentioning

confidence: 99%

Autonomous skill-centric testing using deep learning

Hangl¹,

Stabinger²,

Piater³

2017

2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Abstract-Software testing is an important tool to ensure software quality. This is a hard task in robotics due to dynamic environments and the expensive development and timeconsuming execution of test cases. Most testing approaches use model-based and / or simulation-based testing to overcome these problems. We propose model-free skill-centric testing in which a robot autonomously executes skills in the real world and compares it to previous experiences. The skills are selected by maximising the expected information gain on the distribution of erroneous software functions. We use deep learning to model the sensor data observed during previous successful skill executions and to detect irregularities. Sensor data is connected to function call profiles such that certain misbehaviour can be related to specific functions. We evaluate our approach in simulation and in experiments with a KUKA LWR 4+ robot by purposefully introducing bugs to the software. We demonstrate that these bugs can be detected with high accuracy and without the need for the implementation of specific tests or task-specific models.

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“…In 2012, a generic robot database was developed based on the MongoDb database architecture and ROS middleware [24]. The database provides a long term memory to store and maintain raw data.…”

Section: Related Workmentioning

confidence: 99%

“…To summarise, we subscribe to the idea of separating the episodic long term memory from reasoning and inference mechanisms [16,24]. Our memory is generic, allowing different robots to access a full episodic memory [7].…”

Section: Related Workmentioning

confidence: 99%

Lifelong Augmentation of Multimodal Streaming Autobiographical Memories

Petit¹,

Fischer²,

Demiris³

2016

IEEE Trans. Cogn. Dev. Syst.

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Abstract-Robot systems that interact with humans over extended periods of time will benefit from storing and recalling large amounts of accumulated sensorimotor and interaction data. We provide a principled framework for the cumulative organisation of streaming autobiographical data so that data can be continuously processed and augmented as the processing and reasoning abilities of the agent develop and further interactions with humans take place. As an example, we show how a kinematic structure learning algorithm reasons a-posteriori about the skeleton of a human hand. A partner can be asked to provide feedback about the augmented memories, which can in turn be supplied to the reasoning processes in order to adapt their parameters. We employ active, multi-modal remembering, so the robot as well as humans can gain insights of both the original and augmented memories. Our framework is capable of storing discrete and continuous data in real-time. The data can cover multiple modalities and several layers of abstraction (e.g. from raw sound signals over sentences to extracted meanings). We show a typical interaction with a human partner using an iCub humanoid robot. The framework is implemented in a platform-independent manner. In particular, we validate its multi platform capabilities using the iCub, Baxter and NAO robots. We also provide an interface to cloud based services, which allow automatic annotation of episodes. Our framework is geared towards the developmental robotics community, as it 1) provides a variety of interfaces for other modules, 2) unifies previous works on autobiographical memory, and 3) is licensed as open source software.

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A generic robot database and its application in fault analysis and performance evaluation

Cited by 36 publications

References 11 publications

Graph-based software knowledge: Storage and semantic querying of domain models for run-time adaptation

Graph-based software knowledge: Storage and semantic querying of domain models for run-time adaptation

Autonomous skill-centric testing using deep learning

Lifelong Augmentation of Multimodal Streaming Autobiographical Memories

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