Software testing is an important tool to ensure software quality. However, testing in robotics is a hard task due to dynamic environments and the expensive development and time-consuming execution of test cases. Most testing approaches use model-based and or simulation-based testing to overcome these problems. We propose a model-free skill-centric testing approach in which a robot autonomously executes skills in the real world and compares it to previous experiences. The robot selects specific skills in order to identify errors in the software by maximising the expected information gain. We use deep learning to model the sensor data observed during previous successful executions of a skill and to detect irregularities. This information is connected to functional profiling data 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 models.
During operation of robots large amounts of data are produced and processed for instance in perception, actuation, or decision making. Nowadays this data is typically volatile and disposed right after use. But this data can be valuable and useful later. Therefore we propose a database system that taps into common robot middleware to record any and all data produced at run-time. We present two examples using this data in fault analysis and performance evaluation and describe real-world experiments run on the domestic service robot HERB. This paper revisits the data-information-knowledge-wisdom (DIKW) hierarchy by examining the articulation of the hierarchy in a number of widely read textbooks, and analysing their statements about the nature of data, information, knowledge, and wisdom. The hierarchy referred to variously as the 'Knowledge Hierarchy', the 'Information Hierarchy' and the 'Knowledge Pyramid' is one of the fundamental, widely recognized and 'taken-for-granted' models in the information and knowledge literatures. It is often quoted, or used implicitly, in definitions of data, information and knowledge in the information management, information systems and knowledge management literatures, but there has been limited direct discussion of the hierarchy. After revisiting Ackoff's original articulation of the hierarchy, definitions of data, information, knowledge and wisdom as articulated in recent textbooks in information systems and knowledge management are reviewed and assessed, in pursuit of a consensus on definitions and transformation processes. This process brings to the surface the extent of agreement and dissent in relation to these definitions, and provides a basis for a discussion as to whether these articulations present an adequate distinction between data, information, and knowledge. Typically information is defined in terms of data, knowledge in terms of information, and wisdom in terms of knowledge, but there is less consensus in the description of the processes that transform elements lower in the hierarchy into those above them, leading to a lack of definitional clarity. In addition, there is limited reference to wisdom in these texts.