Learning Motion Planning Functions using a Linear Transition in the C-space: Networks and Kernels

Motion planning approaches aided by learning schemes have achieved relevant results in the community, particularly in terms of rendering new paths efficiently and adapting to new environments/situations through encoder-decoder frameworks and latent space configurations. This paper evaluates the feasibility of learning motion planning functions for robot manipulators using a linear transition of the configuration space. Our computational experiments involving a relevant set of learning architectures have shown the feasibility and the efficiency in finding motion planning functions that meet user-defined criteria. Our approach contributes to realizing the practical efficiency to tackle the learning-based motion planning problem. Due to the amenability to parallelization schemes, our approach is potential to tackle larger degrees of freedom.