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“Tactile SLAM: Real-time Inference of Shape and Pose from Planar Pushing” by S. Suresh, M. Bauza, K.-T. Yu, J.G. Mangelson, A. Rodriguez, and M. Kaess. In Proc. IEEE Intl. Conf. on Robotics and Automation, ICRA, (Xi'an, China), May 2021, pp. 11322-11328. Best service robotics paper finalist (one of four).
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.
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BibTeX entry:
@inproceedings{Suresh21icra,
author = {S. Suresh and M. Bauza and K.-T. Yu and J.G. Mangelson and A.
Rodriguez and M. Kaess},
title = {Tactile {SLAM}: Real-time Inference of Shape and Pose from
Planar Pushing},
booktitle = {Proc. IEEE Intl. Conf. on Robotics and Automation, ICRA},
pages = {11322-11328},
address = {Xi'an, China},
month = may,
year = {2021},
note = {Best service robotics paper finalist (one of four).}
}