A Unifying Variational Framework for Gaussian Process Motion Planning

arXiv:2309.00854v2 Announce Type: replace-cross
Abstract: To control how a robot moves, motion planning algorithms must compute paths in high-dimensional state spaces while accounting for physical constraints related to motors and joints, generating smooth and stable motions, avoiding obstacles, and preventing collisions. A motion planning algorithm must therefore balance competing demands, and should ideally incorporate uncertainty to handle noise, model errors, and facilitate deployment in complex environments. To address these issues, we introduce a framework for robot motion planning based on …

abstract accounting algorithm algorithms arxiv balance compute constraints control cs.lg cs.ro framework motion planning obstacles planning process robot spaces state type