标题： 冗余机器人学习任务执行层次结构 显示英文标题

标题： Learning Task Execution Hierarchies for Redundant Robots

摘要： 现代机器人系统，如移动机械臂、人形机器人和带手臂的空中机器人，通常具有高冗余度，使其能够同时执行多项任务。 管理这种冗余是实现可靠和灵活行为的关键。 一种广泛使用的方法是任务堆叠（SoT），它在统一框架内按优先级组织控制目标。 然而，传统的SoT是由专家手动设计的，限制了它们的适应性和可访问性。 本文介绍了一种新框架，可以从用户定义的目标中自动学习SoT的层次结构和参数。 通过结合强化学习和遗传编程，系统在无需人工干预的情况下发现任务优先级和控制策略。 基于直观指标的成本函数，如精度、安全性和执行时间，指导学习过程。 我们通过在移动-YuMi平台上的仿真和实验验证了我们的方法，该平台是一个高冗余的双臂移动机械臂。 结果表明，学习到的SoT使机器人能够动态适应变化的环境和输入，在保持强大任务执行的同时平衡竞争目标。 这种方法为复杂机器人的冗余管理提供了一个通用且用户友好的解决方案，推动了以人为中心的机器人编程，并减少了对专家设计的需求。 显示英文摘要

摘要： Modern robotic systems, such as mobile manipulators, humanoids, and aerial robots with arms, often possess high redundancy, enabling them to perform multiple tasks simultaneously. Managing this redundancy is key to achieving reliable and flexible behavior. A widely used approach is the Stack of Tasks (SoT), which organizes control objectives by priority within a unified framework. However, traditional SoTs are manually designed by experts, limiting their adaptability and accessibility. This paper introduces a novel framework that automatically learns both the hierarchy and parameters of a SoT from user-defined objectives. By combining Reinforcement Learning and Genetic Programming, the system discovers task priorities and control strategies without manual intervention. A cost function based on intuitive metrics such as precision, safety, and execution time guides the learning process. We validate our method through simulations and experiments on the mobile-YuMi platform, a dual-arm mobile manipulator with high redundancy. Results show that the learned SoTs enable the robot to dynamically adapt to changing environments and inputs, balancing competing objectives while maintaining robust task execution. This approach provides a general and user-friendly solution for redundancy management in complex robots, advancing human-centered robot programming and reducing the need for expert design.