自动学习冗余机器人的任务管理框架，无需手动调参。
Learning Stack-of-Tasks Management for Redundant Robots
- 用遗传编程结合仿真优化任务优先级与参数。
- 多目标平衡下实现精准避障与高跟踪精度。
- 适合需要快速部署的复杂机器人系统设计者。
本文提出一种新框架，可自动学习冗余机器人系统的完整堆叠任务（SoT）控制器，包括任务优先级、激活逻辑和控制参数。不同于传统需人工定义与调优的任务层级，本方法直接从用户指定的成本函数中优化整个SoT结构，该函数编码了安全、精度、操作性或执行速度等直观偏好。方法结合遗传编程与基于仿真的评估，同时探索离散（优先级顺序、任务激活）与连续（增益、轨迹时长）组件。在双臂移动机械臂（ABB移动-YuMi科研平台）上验证，结果表明该框架在多种成本定义下均能稳健收敛，自动抑制无关任务，并对干扰具有强鲁棒性。所学的SoT展现出专家级层次结构，能自然适应多目标权衡。关键的是，所有控制器从Gazebo仿真直接迁移到真实机器人，无需额外调校，实现安全精确运动。静态与动态环境实验显示可靠避障、高跟踪精度及面对人类时行为可预测。该方法为手动设计提供可解释、可扩展的替代方案，支持复杂机器人系统快速生成用户驱动的任务执行层级。
原文摘要 · Abstract (English)
This paper presents a novel framework for automatically learning complete Stack-of-Tasks (SoT) controllers for redundant robotic systems, including task priorities, activation logic, and control parameters. Unlike classical SoT pipelines-where task hierarchies are manually defined and tuned-our approach optimizes the full SoT structure directly from a user-specified cost function encoding intuitive preferences such as safety, precision, manipulability, or execution speed. The method combines Genetic Programming with simulation-based evaluation to explore both discrete (priority order, task activation) and continuous (gains, trajectory durations) components of the controller. We validate the framework on a dual-arm mobile manipulator (the ABB mobile-YuMi research platform), demonstrating robust convergence across multiple cost definitions, automatic suppression of irrelevant tasks, and strong resilience to distractors. Learned SoTs exhibit expert-like hierarchical structure and adapt naturally to multi-objective trade-offs. Crucially, all controllers transfer from Gazebo simulation to the real robot, achieving safe and precise motion without additional tuning. Experiments in static and dynamic environments show reliable obstacle avoidance, high tracking accuracy, and predictable behavior in the presence of humans. The proposed method provides an interpretable and scalable alternative to manual SoT design, enabling rapid, user-driven generation of task execution hierarchies for complex robotic systems.
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