KG-Planner: Knowledge-Informed Graph Neural Planning for Collaborative Manipulators

TL;DR

KG-Planner introduces a knowledge-informed graph neural network for efficient, collision-free robot motion planning in complex environments, explicitly modeling workspace elements to improve planning performance in static and dynamic scenarios.

Contribution

This work presents a novel graph neural planner that integrates explicit workspace knowledge into the planning process, enhancing efficiency and optimality over traditional methods.

Findings

Outperforms existing motion planners in static environments.

Effective in dynamic environments with humans.

Achieves near-optimal robot motions with improved computational efficiency.

Abstract

This paper presents a novel knowledge-informed graph neural planner (KG-Planner) to address the challenge of efficiently planning collision-free motions for robots in high-dimensional spaces, considering both static and dynamic environments involving humans. Unlike traditional motion planners that struggle with finding a balance between efficiency and optimality, the KG-Planner takes a different approach. Instead of relying solely on a neural network or imitating the motions of an oracle planner, our KG-Planner integrates explicit physical knowledge from the workspace. The integration of knowledge has two key aspects: (1) we present an approach to design a graph that can comprehensively model the workspace's compositional structure. The designed graph explicitly incorporates critical elements such as robot joints, obstacles, and their interconnections. This representation allows us to capture the intricate relationships between these elements. (2) We train a Graph Neural Network (GNN) that excels at generating nearly optimal robot motions. In particular, the GNN employs a layer-wise propagation rule to facilitate the exchange and update of information among workspace elements based on their connections. This propagation emphasizes the influence of these elements throughout the planning process. To validate the efficacy and efficiency of our KG-Planner, we conduct extensive experiments in both static and dynamic environments. These experiments include scenarios with and without human workers. The results of our approach are compared against existing methods, showcasing the superior performance of the KG-Planner. A short video introduction of this work is available (video link provided in the paper).