Tactile SLAM: Real-time Inference of Shape and Pose from Planar Pushing

Download: PDF.

“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).

Abstract

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.

Download: PDF.

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).}
}
Last updated: November 7, 2021