Multi Objective Graph Heuristic Search for Terrestrial Robot Design
We present methods for co designing rigid robots over control and morphology (including discrete topology) over multiple objectives. Previous work has addressed problems in single objective robot co design or multi objective control. However, the joint multi objective co design problem is extremely important for generating capable, versatile, algorithmically designed robots. In this work, we present Multi Objective Graph Heuristic Search, which extends a single objective graph heuristic search from previous work to enable a highly efficient multi objective search in a combinatorial design topology space. Core to this approach, we introduce a new universal, multiobjective heuristic function based on graph neural networks that is able to effectively leverage learned information between different task trade offs. We demonstrate our approach on six combinations of seven terrestrial locomotion and design tasks, including one three objective example. We compare the captured Pareto fronts across different methods and demonstrate that our multi objective graph heuristic search quantitatively and qualitatively outperforms other techniques.
ArXiv