リーマン運動ポリシーを用いたマルチロボット系の多目的ポリシー生成
Multi-Objective Policy Generation for Multi-Robot Systems Using Riemannian Motion Policies
多目的タスクをサブタスクに分解し、各サブタスクの制御器をリーマン運動ポリシーとして設計・統合することで、ロボット間やサブタスク間の相互作用を暗黙的に扱うマルチロボット制御ポリシー生成フレームワークを提案した。
著者: Anqi Li, Mustafa Mukadam, Magnus Egerstedt, Byron Boots
分類: cs.RO
原文アブストラクト
In many applications, multi-robot systems are required to achieve multiple objectives. For these multi-objective tasks, it is oftentimes hard to design a single control policy that fulfills all the objectives simultaneously. In this paper, we focus on multi-objective tasks that can be decomposed into a set of simple subtasks. Controllers for these subtasks are individually designed and then combined into a control policy for the entire team. One significant feature of our work is that the subtask controllers are designed along with their underlying manifolds. When a controller is combined with other controllers, their associated manifolds are also taken into account. This formulation yields a policy generation framework for multi-robot systems that can combine controllers for a variety of objectives while implicitly handling the interaction among robots and subtasks. To describe controllers on manifolds, we adopt Riemannian Motion Policies (RMPs), and propose a collection of RMPs for common multi-robot subtasks. Centralized and decentralized algorithms are designed to combine these RMPs into a final control policy. Theoretical analysis shows that the system under the control policy is stable. Moreover, we prove that many existing multi-robot controllers can be closely approximated by the framework. The proposed algorithms are validated through both simulated tasks and robotic implementations.
