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 openly available to the research community. Specifically, we recorded episodic memories with rich semantic annotations during a pizza preparation experiment in autonomous robot manipulation. Afterwards, we released them as an open knowledge base using the cloud- and web-based robot knowledge service OPENEASE. We discuss several ways on how this open data can be used to validate our experimental reports and to tackle novel challenging research problems.

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 leading-edge autonomous robotic agents. It also provides the representational infrastructure to make inhomogeneous experience data from robots and human manipulation episodes semantically accessible, and is complemented by a suite of software tools that enable researchers and robots to interpret, analyze, visualize, and learn from the experience data. Using Open-EASE users can retrieve the memorized experiences of manipulation episodes and ask queries regarding to what the robot saw, reasoned, and did as well as how the robot did it, why, and what effects it caused.

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 framework that gives robot agents an intuition of their actions' effects and for choosing parameter values that have proven to be useful before. We let a robot form these predictions and expectations from episodic memories collected during earlier plan executions, improving its own behavior with every new situation encountered. We evaluate and explain our approach using experiments performed on a PR2 robot performing complex mobile manipulation activities in a kitchen environment.