UpViTaL: Unpaired Visual-Tactile Self-Supervised Representation Learning for Dexterous Robotic Manipulation

Abstract

Visual and tactile pretraining have been extensively studied in dexterous robot manipulation tasks. However, existing methods typically require the simultaneous acquisition of visual and tactile data, making it difficult to utilize low-cost, unpaired visual-tactile datasets. Moreover, these methods often rely on tactile sensors to provide input data for reinforcement learning (RL) during the physical deployment of robotic dexterous hands, which highly increases deployment costs. To address these challenges, we propose UpViTaL, an unpaired visualtactile self-supervised representation learning method for RLbased robot dexterous manipulation. Specifically, we collect low-cost unpaired visual and tactile datasets for manipulation skill learning using a camera and tactile gloves on three robot manipulation tasks. The temporal tactile self-supervised representation learning module of UpViTaL is used to explore efficient tactile representations from time-series tactile data. In parallel, the visual pretraining module of UpViTaL helps to extract efficient visual representations from visual data. In addition, we fuse unpaired visual-tactile representations through an RL reward mechanism, which does not require robotic dexterous hands tactile sensors for practical deployment. We validate our approach on three dexterous robot manipulation tasks. Experimental results demonstrate that UpViTaL can efficiently learn robot manipulation skills. Compared to existing approaches for visual pretraining, our method significantly improves the success rate by more than 30%.