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. To appear.

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},
   address = {Xi'an, China},
   month = may,
   year = {2021},
   note = {To appear}
}
Last updated: March 26, 2021