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

Tactile perception is central to robot manipulation in unstructured environments. However, it requires contact, and a mature implementation must infer object models while also accounting for the motion induced by the interaction. In this work, we present a method to estimate both object shape and pose in real-time from a stream of tactile measurements. This is applied towards tactile exploration of an unknown object by planar pushing. We consider this as an online SLAM problem with a nonparametric shape representation. Our formulation of tactile inference alternates between Gaussian process implicit surface regression and pose estimation on a factor graph. Through a combination of local Gaussian processes and fixed-lag smoothing, we infer object shape and pose in real-time. We evaluate our system across different objects in both simulated and real-world planar pushing tasks.

Automated summary

The research presents a method to estimate object shape and pose in real-time from tactile measurements, applied to tactile exploration of unknown objects through planar pushing. The method combines Gaussian process implicit surface regression and pose estimation on a factor graph to infer object shape and pose in real-time. The system is evaluated across different objects in simulated and real-world planar pushing tasks.