PairwiseNet: Pairwise Collision Distance Learning for High-dof Robot Systems

[ Information ].

International Conference on Robot Learning, 2023.

[ Authors ]

Jihwan Kim and Frank C. Park

[ Abstract ]

Motion planning of robot manipulation systems operating in complex environments remains a challenging problem, requiring evaluation of the collision distance and its derivative. Due to its computational complexity, recent studies have attempted to utilize data-driven approaches to learn the collision distance. However, their performance degrades significantly for complicated high-dof systems like multi-arm robots. Also, the model must be retrained every time the environment undergoes even slight changes. In this paper, we propose PairwiseNet, a model that estimates the minimum distance between two geometric shapes and overcomes many of the limitations of current models. By dividing the problem of global collision distance learning into smaller pairwise sub-problems, PairwiseNet can be used to efficiently the global collision distance. PairwiseNet can be used without further modifications or training for any system comprised of the same shape elements (such as in the training dataset). Experiments with multi-arm manipulation systems of various dof show that our model achieves significant performance improvements with respect to several performance metrics, especially the false positive rate with the collision-free guaranteed threshold. Results further demonstrate that our single trained PairwiseNet model is applicable to all multi-arm systems used in the evaluation.