  We introduce GEOTACT, a robotic manipulation method capable of retrieving
objects buried in granular media. This is a challenging task due to the need to
interact with granular media, and doing so based exclusively on tactile
feedback, since a buried object can be completely hidden from vision. Tactile
feedback is in itself challenging in this context, due to ubiquitous contact
with the surrounding media, and the inherent noise level induced by the tactile
readings. To address these challenges, we use a learning method trained
end-to-end with simulated sensor noise. We show that our problem formulation
leads to the natural emergence of learned pushing behaviors that the
manipulator uses to reduce uncertainty and funnel the object to a stable grasp
despite spurious and noisy tactile readings. We also introduce a training
curriculum that enables learning these behaviors in simulation, followed by
zero-shot transfer to real hardware. To the best of our knowledge, GEOTACT is
the first method to reliably retrieve a number of different objects from a
granular environment, doing so on real hardware and with integrated tactile
sensing. Videos and additional information can be found at
https://jxu.ai/geotact.
