Abstract:The digital transformation of production requires new methods of data integration and storage, as well as decision making and support systems that work vertically and horizontally throughout the development, production, and use cycle. In this paper, we propose Data-to-Knowledge (and Knowledge-to-Data) pipelines for production as a universal concept building on a network of Digital Shadows (a concept augmenting Digital Twins). We show a proof of concept that builds on and bridges existing infrastructure to 1) capture and semantically annotates trajectory data from multiple similar but independent robots in different organisations and use cases in a data lakehouse and 2) an independent process that dynamically queries matching data for training an inverse dynamic foundation model for robotic control. The article discusses the challenges and benefits of this approach and how Data-to-Knowledge pipelines contribute efficiency gains and industrial scalability in a World Wide Lab as a research outlook.
Abstract:Robotic assistance in robot arm teleoperation tasks has recently gained a lot of traction in industrial and domestic environment. A wide variety of input devices is used in such setups. Due to the noise in the input signals (e.g., Brain Computer Interface (BCI)) or delays due to environmental conditions (e.g., space robot teleoperation), users need assistive autonomy that keeps them in control while following predefined trajectories and avoids obstacles. This assistance calls for activity representations that are easy to define by the operator and able to take the dynamic world state into consideration. This paper represents Activities of Daily Living using Behavior Trees (BTs) whose inherent readability and modularity enables an end user to define new activities using a simple interface. To achieve this, we augment BTs with Shared Control Action Nodes, which guide the user's input on a trajectory facilitating and ensuring task execution.