混合整数計画法による最適制約付きタスクプランニング
Optimal Constrained Task Planning as Mixed Integer Programming
ロボットのタスクプランニングを混合整数凸計画問題として定式化し、市販のMIPソルバで解くことで、数値制約を満たしつつ目的関数に対して最適な行動列を生成する手法を提案。移動マニピュレーションタスクでシミュレーションと実機ヒューマノイドにより評価した。
著者: Alphonsus Adu-Bredu, Nikhil Devraj, Odest Chadwicke Jenkins
分類: cs.RO
原文アブストラクト
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
