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— Making future autonomous robots capable of accomplishing human-scale manipulation tasks requires us to equip them with knowledge and reasoning mechanisms. We propose OPEN-EASE, a remote knowledge representation and processing service that aims at facilitating these capabilities. OPEN-EASE gives its users unprecedented access to the knowledge of(More)
In this paper we discuss how the combination of modern technologies in “big data” storage and management, knowledge representation and processing, cloud-based computation, and web technology can help the robotics community to establish and strengthen an open research discipline. We describe how we made the demonstrator of a EU project review(More)
This paper investigates issues in the plan design of cognition-enabled robotic agents performing everyday manipulation tasks. We believe that plan languages employed by most cognitive archi-tectures are syntactically too restricted to specify the flexibility, generality, and robustness needed to perform physical manipulation tasks. As a consequence, the(More)
Autonoumous robotic agents performing human-scale manipulation activities in open environments will need extensive knowledge processing capabilities. In previous work we presented OPENEASE, an online knowledge representation and reasoning framework with interfaces for both autonomous agent systems and robotics researchers. We propose to hold an interactive(More)
Autonomous robots in unstructured and dynamically changing retail environments have to master complex perception, knowledge processing, and manipulation tasks. To enable them to act competently, we propose a framework based on three core components: (•) A background knowledge enabled perception system, which is capable of combining diverse information(More)
We present Open-EASE, a cloud-based knowledge base of robot experience data that can serve as episodic memory, providing a robot with comprehensive information for autonomously learning manipulation tasks. Open-EASE combines both robot and human activity data in a common, semantically annotated knowledge base, including robot poses, object information,(More)
Autonomous robotic agents acting in open environments have to master situations and action effects they did not anticipate. To deal with these issues we propose an information processing concept for plan interpretation that is based on three concepts: • statically defined, and dynamically inferred knowledge, • context-definition on a task level to(More)
Robots performing general purpose plans must deal with a wide variety of contexts. Situations they encounter might differ only in subtle, but important details in context and parameterization that have a massive impact on an action's outcome. To avoid the effort of encoding all possible combinations of subtleties into plans, we present a prediction(More)
With quickly progressing and increasingly complex robot control and reasoning systems, a large gap of practical real-world knowledge for robots needs to be filled. While two prominent directions exist, namely designing all knowledge manually, or completely bootstrapping it, we emphasize the combination of both: Starting with simple heuristics, we let robots(More)
Autonomous robots in unstructured and dynamically changing retail environments have to master complex perception, knowledge processing, and manipulation tasks. To enable them to act competently , we propose a framework based on three core components: (•) a knowledge-enabled perception system, capable of combining diverse information sources to cope with(More)
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