Closing the Motion Execution Gap: From Semantic Motion Task Constraints to Kinematic Control
Simon Stelter, Vanessa Hassouna, Malte Huerkamp, Michael Beetz
Abstract
This paper addresses the Motion Execution Gap, the disconnect between high-level symbolic task descriptions using semantic constraints and executable robot motions. \textbf{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 \ac{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).
BibTeX
@inproceedings{ijcai2026_closingthemotion,
title = {Closing the Motion Execution Gap: From Semantic Motion Task Constraints to Kinematic Control},
author = {Simon Stelter and Vanessa Hassouna and Malte Huerkamp and Michael Beetz},
booktitle = {IJCAI 2026},
year = {2026}
}