让机器人从语义指令直接执行复杂动作，跨平台通用。
Closing the Motion Execution Gap: From Semantic Motion Task Constraints to Kinematic Control
- 用状态机统一表达动作约束，支持并行与嵌套
- 通过可微世界模型实现跨机器人泛化，部署于8种平台
- 基于lMPC保证任务切换时平滑过渡，适合工业应用
本文解决高阶语义任务描述与可执行运动之间的运动执行鸿沟问题。提出运动状态图作为复杂动作的可执行符号表示，支持任意排列动作约束、监控器或嵌套状态图，实现并行与顺序组合。通过统一可微分的机器人与环境运动世界模型，实现以世界为中心的动作描述及跨机器人本体的泛化能力。运动执行采用基于lMPC的任务函数方法，利用加加速度（jerk）约束确保任务切换时的平滑过渡。在8个不同机器人平台上成功部署，验证了跨平台可移植性。所提框架命名为Giskard，开源地址：https://github.com/cram2/cognitive_robot_abstract_machine。
原文摘要 · Abstract (English)
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.
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