CRESTA: A Cognitivist Robot Execution framework for Semantic-driven Task Awareness

The advancement of autonomous robots is still in need of a comprehensive framework for task execution awareness, enabling the generation of autonomous behaviors and responses in unknown dynamic environments. Situation awareness aims to enhance robot autonomy and adaptation during task execution by effectively combining key capabilities, such as reasoning, planning, projection of actions’ effects into future states, as well as perception and comprehension of the surroundings. In this work, we propose CRESTA, a novel cognitivist framework for semantic-driven task awareness that addresses the intricate challenges of perceiving, navigating, and manipulating dynamic environments. CRESTA's objective of achieving effective robot awareness relies on the perceived environment semantics and on the combined use of online planning, reasoning, and monitoring, while also enabling recovery from task-level failures. It is designed as a set of online modules for (a) collecting and analyzing multi-sensor data as well as updating the world model description, (b) real-time decision-making and task states monitoring, and (c) execution of each action. Being highly modular and configurable to assorted robotic systems, the proposed framework aims for adaptability across diverse robotic platforms and tasks. In this work, a detailed description of CRESTA's framework comes along with demonstrative tasks to showcase its capabilities on both the CENTAURO robot and on a custom 6 DoF manipulator. In the discussed experimental results, CRESTA leads the robot to open a door or to navigate and manipulate a lever, while recovering from failures by adapting the parameters of its actions.