Multimodal Robotic Perception Integrating Visual Spatial And Tactile Sensing Through Deep Learning Architectures

Abstract

Autonomous robotic systems operating in unstructured, real-world environments require the seamless integration of heterogeneous sensory inputs — visual, spatial, and tactile — to achieve the level of situational awareness necessary for complex manipulation and navigation tasks. This survey provides a systematic and comprehensive review of the state-of-the-art in multimodal robotic perception, spanning the period 2017–2025. We examine the evolution from convolutional neural networks (CNNs) to Vision Transformers (ViTs) for visual processing; the deep-learning-based analysis of 3D point clouds via PointNet-family architectures, voxel- and pillar-based methods, and LiDAR-camera fusion paradigms; and the emergence of high-resolution visuotactile sensors (GelSight, DIGIT, TacTip) coupled with learned tactile representations for dexterous manipulation. We then survey multimodal fusion strategies — early, late, and intermediate — with emphasis on cross-modal attention mechanisms and joint embedding spaces. Finally, we analyse Vision-Language-Action (VLA) models as the frontier of embodied multimodal intelligence. For each sub-domain, we discuss open benchmarks, quantitative results, and the principal technical challenges that remain unsolved. Our analysis identifies four key research gaps: efficient fusion architectures for embedded hardware, self-supervised tactile representation learning, spatio-temporal fusion of asynchronous modalities, and out-of-distribution robustness evaluation in real robotic settings. This survey is intended as a reference for researchers and practitioners working at the intersection of deep learning and robotics.