Closing the Motion Execution Gap: From Semantic Motion Task Constraints to Kinematic Control

TL;DR

This paper presents Giskard, a framework that bridges high-level semantic motion constraints and robot kinematic control, enabling cross-platform deployment and smooth motion execution.

Contribution

It introduces Motion Statecharts and a unified differentiable kinematic world model for flexible, generalizable robot motion specification and execution.

Findings

Successfully deployed on eight robot platforms in diverse environments.

Ensured smooth transitions during task switches with jerk bounds.

Open source implementation available at the provided GitHub link.

Abstract

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.