  Tactile perception is a critical component of solving real-world manipulation
tasks, but tactile sensors for manipulation have barriers to use such as
fragility and cost. In this work, we engage a robust, low-cost tactile sensor,
BeadSight, as an alternative to precise pre-calibrated sensors for a
pretraining approach to manipulation. We show that tactile pretraining, even
with a low-fidelity sensor as BeadSight, can improve an imitation learning
agent's performance on complex manipulation tasks. We demonstrate this method
against a baseline USB cable plugging task, previously achieved with a much
higher precision GelSight sensor as the tactile input to pretraining. Our best
BeadSight pretrained visuo-tactile agent completed the task with 70\% accuracy
compared to 85\% for the best GelSight pretrained visuo-tactile agent, with
vision-only inference for both.
