Service robots will have to accomplish more and more complex, open-ended tasks and regularly acquire new skills. In this work, we propose a new approach to generating plans for such household robots. Instead composing them from atomic actions, we propose to transform task descriptions on web sites like ehow.com into executable robot plans. We present methods for automatically converting the instructions given in natural language into a formal, logic-based representation, for resolving the word senses using the WordNet database and the Cyc ontology, and for exporting the generated plans into the mobile robot's plan language RPL. We discuss the problems of inferring information missing in these descriptions, of grounding the abstract task descriptions in the perception and action system, and we propose techniques for solving them. The whole system works autonomously without human interaction. It has successfully been tested with a set of about 150 natural language directives, of which up to 80% could be correctly transformed.

We present ROBOSHERLOCK, an open source software framework for implementing perception systems for robots performing human-scale everyday manipulation tasks. In ROBOSHERLOCK, perception and interpretation of realistic scenes is formulated as an unstructured information management (UIM) problem. The application of the UIM principle supports the implementation of perception systems that can answer task-relevant queries about objects in a scene, boost object recognition performance by combining the strengths of multiple perception algorithms, support knowledge-enabled reasoning about objects and enable automatic and knowledgedriven generation of processing pipelines. We demonstrate the potential of the proposed framework by three feasibility studies of systems for real-world scene perception that have been built on top of ROBOSHERLOCK.

In this paper we discuss the problem of actionspecific knowledge processing, representation and acquisition by autonomous robots performing everyday activities. We report on a thorough analysis of the household domain which has been performed on a large corpus of natural-language instructions from the Web, which underlines the supreme need of action-specific knowledge for robots acting in those environments. We introduce the concept of Probabilistic Robot Action Cores (PRAC) that are well-suited for encoding such knowledge in a probabilistic first-order knowledge base. We additionally show how such a knowledge base can be acquired by natural language and we address the problems of incompleteness, underspecification and ambiguity of naturalistic action specifications and point out how PRAC models can tackle those.

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