Multi-Layered Reasoning from a Single Viewpoint for Learning See-Through Grasping

Sensory substitution enables biological systems to perceive stimuli that are typically perceived by another organ, which is inspirational for physical agents. Multimodal perception of intrinsic and extrinsic interactions is critical in building an intelligent robot that learns. This study presents a Vision-based See-Through Perception (VBSeeThruP) architecture that simultaneously perceives multiple intrinsic and extrinsic modalities from a single visual input, in a markerless manner, all packed into a soft robotic finger using the Soft Polyhedral Network design. It is generally applicable to miniature vision systems placed beneath deformable networks with a see-through design, capturing real-time images of the network's physical interactions induced by contact-based events, overlaid on the visual scene of the external environment, as demonstrated in the ablation study. We present the VBSeeThruP's capability for learning reactive grasping without using external cameras or dedicated force and torque sensors on the fingertips. Using the inpainted scene and the deformation mask, we further demonstrate the multimodal performance of the VBSeeThruP architecture to simultaneously achieve various perceptions, including but not limited to scene inpainting, object detection, depth sensing, scene segmentation, masked deformation tracking, 6D force/torque sensing, and contact event detection, all within a single sensory input from the in-finger vision markerlessly.