Context in Robotics and Information Fusion

Abstract

Robotics systems need to be robust and adaptable to multiple operational conditions, in order to be deployable in different application domains. Contextual knowledge can be used for achieving greater flexibility and robustness in tackling the main tasks of a robot, namely mission execution, adaptability to environmental conditions, and self-assessment of performance. In this chapter, we review the research work focusing on the acquisition, management, and deployment of contextual information in robotic systems. Our aim is to show that several uses of contextual knowledge (at different representational levels) have been proposed in the literature, regarding many tasks that are typically required for mobile robots. As a result of this survey, we analyze which notions and approaches are applicable to the design and implementation of architectures for information fusion. More specifically, we sketch an architectural framework which enables for an effective engineering of systems that use contextual knowledge, by including the acquisition, representation, and use of contextual information into a framework for information fusion

Learning Context-Adaptive Task Constraints for Robotic Manipulation

“…Next we evaluate the capability of the approach to estimate suitable task weights and adapt them (a) over time (during task execution), (b) with respect to different constraint variables and (c) with respect to different contexts. As described in section 4, we estimate the task weights from the inter-demonstration variance of the normalized data according to Equation (7). A large variance corresponds to small task weights and vice versa.…”

“…Generally spoken, context in robotics can be defined as "a configuration of features which are (...) useful to influence the decision process of a robotic system" [7]. Approaches that are able to automatically adapt the robot controls with respect to such changes are referred to as context-adaptive.…”

A context‐awareness model for activity recognition in robot‐assisted scenarios

“…At a high level, one of the most extended approaches for solving context‐awareness scenarios is Turner's work (Turner, ), which proposes context inference using (a) environmental information, (b) information about the tasks, and (c) agent self‐knowledge. At a practical level, available solutions are ad hoc (Bloisi, Nardi, Riccio, & Trapani, ) approaches, given the application scenarios. This research proposes generalizing and including new contexts, which also affect how decisions are made.…”

A Robotic Context Query-processing Framework based on Spatio-temporal Context Ontology

“…However, for intelligent service robots, the concrete context knowledge of the robot's domain needs to be redefined. Turner [2] defined context knowledge for intelligent agent in three categories, based on which Bloisi [3] redefined robot-oriented context knowledge: environmental knowledge, task-related knowledge, and self-knowledge. Environmental knowledge includes physical-space information about the environment outside the robot, such as locations of people and objects and environmental map.…”