“While it is generally acknowledged that force feedback is beneficial to robotic control, applications of policy learning to robotic manipulation typically only leverage visual feedback. Recently, symmetric neural models have been used to significantly improve the sample efficiency and performance of policy learning across a variety of robotic manipulation domains. This paper explores an application of symmetric policy learning to visual-force problems. We present Symmetric Visual Force Learning (SVFL), a novel method for robotic control which leverages visual and force feedback.”

Find the paper and full list of authors at ArXiv.