Progress in symmetry preserving robot perception and control through geometry and learning

Abstract

This article reports on recent progress in robot perception and control methods developed by taking the symmetry of the problem into account. Inspired by existing mathematical tools for studying the symmetry structures of geometric spaces, geometric sensor registration, state estimator, and control methods provide indispensable insights into the problem formulations and generalization of robotics algorithms to challenging unknown environments. When combined with computational methods for learning hard-to-measure quantities, symmetry-preserving methods unleash tremendous performance. The article supports this claim by showcasing experimental results of robot perception, state estimation, and control in real-world scenarios.

Keywords: Lie groups; deep learning; equivariant models; equivariant representation learning; geometric control; invariant extended Kalman filter; robot control; robot perception.