Closing the Motion Execution Gap: From Semantic Motion Task Constraints to Kinematic Control
For roboticists, it provides a unified framework for specifying and executing complex motions across diverse robot platforms, reducing the need for platform-specific programming.
The paper introduces Motion Statecharts and the Giskard framework to bridge the gap between high-level semantic task descriptions and executable robot motions, demonstrating cross-platform transferability on eight robot platforms.
This paper addresses the Motion Execution Gap, the disconnect between high-level symbolic task descriptions using semantic constraints and executable robot motions. Motion Statecharts are introduced as an executable symbolic representation for complex motions. They allow the arbitrary arrangement of motion constraints, monitors or nested statecharts in parallel and sequence. World-centric motion specification and generalization across embodiments are enabled through the use of a unified differentiable kinematic world model of both, robots and environments. Motion execution is realized through a lMPC-based implementation of the task-function approach, in which smooth transitions during task switches are ensured using jerk bounds. Cross-platform transferability was demonstrated by deploying the method on eight robot platforms, operating in diverse environments. The proposed framework is called Giskard and is available open source: https://github.com/cram2/cognitive_robot_abstract_machine.