Visuotactile-RL: Learning Multimodal Manipulation Policies with Deep Reinforcement Learning
This paper focuses on the problem setting where both visual and tactile sensors provide pixel-level feedback for Visuotactile reinforcement learning agents, and investigates the challenges associated with multimodal learning and proposes several improvements to existing RL methods.
Abstract
Manipulating objects with dexterity requires timely feedback that simultaneously leverages the senses of vision and touch. In this paper, we focus on the problem setting where both visual and tactile sensors provide pixel-level feedback for Visuotactile reinforcement learning agents. We investigate the challenges associated with multimodal learning and propose several improvements to existing RL methods; including tactile gating, tactile data augmentation, and visual degradation. When compared with visual-only and tactile-only baselines, our Visuotactile-RL agents showcase (1) significant improvements in contact-rich tasks; (2) improved robustness to visual changes (lighting/camera view) in the workspace; and (3) resilience to physical changes in the task environment (weight/friction of objects).