论文标题
最佳约束任务计划作为混合整数编程
Optimal Constrained Task Planning as Mixed Integer Programming
论文作者
论文摘要
为了使机器人成功执行分配给他们的任务，他们必须能够计划正确的操作顺序。这些行动在指定的目标方面必须是最佳的，并且可以满足其世界上存在的任何约束。我们提出了一种用于机器人任务计划的方法，该方法能够计划在给定特定目标函数的同时满足所有指定的数值约束的同时，可以计划完成接地动作的最佳顺序。我们的方法通过将整个任务计划问题编码为单个混合整数凸面程序来实现这一目标，然后使用现成的混合整数编程求解器来解决该问题。我们在模拟和物理类人形机器人的几个移动操作任务上评估了我们的方法。我们的方法能够始终如一地制定最佳计划，同时考虑移动操作任务中所有指定的数值约束。可以在此URL上找到我们方法组成部分的开源实现以及在模拟和物理世界中执行计划的扎根动作的机器人的视频：https：//adubredu.github.io/gtpmip
For robots to successfully execute tasks assigned to them, they must be capable of planning the right sequence of actions. These actions must be both optimal with respect to a specified objective and satisfy whatever constraints exist in their world. We propose an approach for robot task planning that is capable of planning the optimal sequence of grounded actions to accomplish a task given a specific objective function while satisfying all specified numerical constraints. Our approach accomplishes this by encoding the entire task planning problem as a single mixed integer convex program, which it then solves using an off-the-shelf Mixed Integer Programming solver. We evaluate our approach on several mobile manipulation tasks in both simulation and on a physical humanoid robot. Our approach is able to consistently produce optimal plans while accounting for all specified numerical constraints in the mobile manipulation tasks. Open-source implementations of the components of our approach as well as videos of robots executing planned grounded actions in both simulation and the physical world can be found at this url: https://adubredu.github.io/gtpmip
