Multi-Objective Policy Generation for Multi-Robot Systems Using Riemannian Motion Policies
This work addresses the challenge of multi-objective control for multi-robot systems, offering a novel framework that is incremental in building upon existing RMP methods.
The paper tackles the problem of designing control policies for multi-robot systems with multiple objectives by decomposing tasks into subtasks and combining controllers using Riemannian Motion Policies (RMPs), resulting in a framework that ensures system stability and approximates existing controllers, validated through simulations and robotic implementations.
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.