Symmetric Models for Visual Force Policy Learning

TL;DR

This paper introduces SVFL, a novel symmetric policy learning method that effectively combines visual and force feedback to improve robotic manipulation, especially in low visual clarity scenarios.

Contribution

It presents a new symmetric learning approach for visual-force control, demonstrating superior performance over existing methods in robotic manipulation tasks.

Findings

SVFL outperforms state-of-the-art baselines in visual-force learning.

Learning force feedback control is beneficial in low visual acuity scenarios.

Symmetric models improve sample efficiency and manipulation success.

Abstract

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. We demonstrate that SVFL can significantly outperform state of the art baselines for visual force learning and report several interesting empirical findings related to the utility of learning force feedback control policies in both general manipulation tasks and scenarios with low visual acuity.