Robotic manipulation requires both rich multimodal
perception and effective learning frameworks to handle complex
real-world tasks. See-Through-Skin (STS) sensors, which
combine tactile and visual perception, offer promising sensing
capabilities, while modern imitation learning provides powerful
tools for policy acquisition. However, existing STS designs lack
simultaneous multimodal perception and suffer from unreliable
tactile tracking. Furthermore, integrating these rich multimodal
signals into learning-based manipulation pipelines remains an
open challenge. We introduce TacThru, an STS sensor enabling
simultaneous visual perception and robust tactile signal extraction,
and TacThru-UMI, an imitation learning framework that
leverages these multimodal signals for manipulation. Our sensor
features a fully transparent elastomer, persistent illumination,
novel keyline markers, and efficient tracking, while our learning
system integrates these signals through a Transformer-based
Diffusion Policy. Experiments on five challenging real-world tasks
show that TacThru-UMI achieves an average success rate of
85.5%, significantly outperforming the baselines of alternating
tactile-visual (66.3%) and vision-only (55.4%). The system excels
in critical scenarios, including contact detection with thin and
soft objects and precision manipulation requiring multimodal
coordination. This work demonstrates that combining simultaneous
multimodal perception with modern learning frameworks
enables more precise, adaptable robotic manipulation.
