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.

翻译：本文针对运动执行鸿沟问题——即高层级符号化任务描述（利用语义约束）与可执行机器人运动之间的脱节。我们提出运动状态图作为复杂运动的可执行符号化表示，允许在并行和序列中任意排列运动约束、监控器或嵌套状态图。通过使用机器人与环境的统一可微运动学世界模型，实现了以世界为中心的运动规范及跨实体泛化。运动执行通过基于线性模型预测控制的任务函数方法实现，其中利用加加速度边界确保任务切换时的平滑过渡。通过在八个不同环境的机器人平台上部署该方法，验证了跨平台可迁移性。所提出的框架命名为Giskard，并以开源形式发布：https://github.com/cram2/cognitive_robot_abstract_machine。