BrainyMP: Enhancing Motion Planning Using Graph Neural Network Inspired by Brain Spatial Relational Memory

Efficient and reliable motion planning is essential for robots in transportation systems. Learning-based motion planners, especially those leveraging graph neural networks (GNNs), have emerged as promising approaches to accelerate motion planning. However, current GNN-based planners suffer from inevitable performance bottlenecks due to their insufficient exploitation of environmental information and the intrinsic connections within graph structures. In contrast, the human brain exhibits innate strengths in decision-making and reasoning, enabling complex inferences from sparse observations and rapid integration of new information to control behavior. Neuroscience evidence reveals the human brain translates decision-making problems into graph structures and sensory observations, leveraging spatial relational memory for sensory inference. To address these issues, this paper proposes a brain-inspired GNN-based motion planner, BrainyMP, which innovatively draws on the brain’s spatial relational memory mechanisms. Specifically, a selective sampling strategy is proposed to reduce unnecessary exploration during the construction of the random geometric graph (RGG). Additionally, a Brainy Edge Selector is designed to filter inappropriate edges, enhancing planning quality. Furthermore, the Memory-aware Predictor is proposed to improve graph pattern learning capabilities and planning efficiency by integrating subgraph structures. Extensive experimental results demonstrate that the proposed method significantly improves planning quality and efficiency in maze and robotic-arm manipulation tasks while maintaining high success rates. These results indicate that emulating human brain mechanisms holds promise for improving robotic performance.