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Tactile perception is central to robot manipulation in unstructuredenvironments. However, it requires contact, and a mature implementation mustinfer object models while also accounting for the motion induced by theinteraction. In this work, we present a method to estimate both object shapeand pose in real-time from a stream of tactile measurements. This is appliedtowards tactile exploration of an unknown object by planar pushing. We considerthis as an online SLAM problem with a nonparametric shape representation. Ourformulation of tactile inference alternates between Gaussian process implicitsurface regression and pose estimation on a factor graph. Through a combinationof local Gaussian processes and fixed-lag smoothing, we infer object shape andpose in real-time. We evaluate our system across different objects in bothsimulated and real-world planar pushing tasks.

Tactile SLAM: Real-time inference of shape and pose from planar pushing