This paper presents a constrained-optimization formulation for the prioritized execution of learned robot tasks. The framework lends itself to the execution of tasks encoded by value functions, such as tasks learned using the reinforcement learning paradigm. The tasks are encoded as constraints of a convex optimization program by using control Lyapunov functions. Moreover, an additional constraint is enforced in order to specify relative priorities between the tasks. The proposed approach is showcased in simulation using a team of mobile robots executing coordinated multi-robot tasks.
翻译:本文件为优先执行学到的机器人任务提供了一个限制优化的配方。 框架可以用于执行由价值函数编码的任务, 如使用强化学习模式学习的任务。 任务被编为使用控制 Lyapunov 函数的convex优化程序的限制。 此外, 还要执行额外的制约, 以具体说明任务之间的相对优先顺序 。 在模拟中, 使用一组执行协调的多机器人任务的移动机器人来展示拟议方法 。