This study explores the intersection of neural networks and classical robotics algorithms through the Neural Algorithmic Reasoning (NAR) framework, allowing to train neural networks to effectively reason like classical robotics algorithms by learning to execute them. Algorithms are integral to robotics and safety-critical applications due to their predictable and consistent performance through logical and mathematical principles. In contrast, while neural networks are highly adaptable, handling complex, high-dimensional data and generalising across tasks, they often lack interpretability and transparency in their internal computations. We propose a Graph Neural Network (GNN)-based learning framework, NAR-*ICP, which learns the intermediate algorithmic steps of classical ICP-based pointcloud registration algorithms, and extend the CLRS Algorithmic Reasoning Benchmark with classical robotics perception algorithms. We evaluate our approach across diverse datasets, from real-world to synthetic, demonstrating its flexibility in handling complex and noisy inputs, along with its potential to be used as part of a larger learning system. Our results indicate that our method achieves superior performance across all benchmarks and datasets, consistently surpassing even the algorithms it has been trained on, further demonstrating its ability to generalise beyond the capabilities of traditional algorithms.

翻译：本研究通过神经算法推理（NAR）框架探索了神经网络与经典机器人算法之间的交叉，该框架使得训练神经网络能够通过学习执行这些算法，从而有效地像经典机器人算法一样进行推理。算法因其通过逻辑和数学原理所展现的可预测且一致的性能，是机器人学及安全关键应用的核心组成部分。相比之下，虽然神经网络具有高度适应性，能够处理复杂的高维数据并在任务间实现泛化，但其内部计算往往缺乏可解释性与透明度。我们提出了一种基于图神经网络（GNN）的学习框架NAR-*ICP，该框架能够学习经典基于ICP的点云配准算法的中间算法步骤，并将CLRS算法推理基准扩展至经典机器人感知算法。我们在从真实世界到合成的多种数据集上评估了所提方法，证明了其在处理复杂及含噪声输入方面的灵活性，以及其作为更大学习系统组成部分的潜力。我们的结果表明，该方法在所有基准测试和数据集上均取得了优越的性能，甚至持续超越了其训练所基于的算法，进一步证明了其能够泛化至超越传统算法能力范围之外。