Tactile SLAM: Real-time inference of shape and pose from planar pushing
Author(s)
Suresh, Sudharshan; Bauza, Maria; Yu, Kuan-Ting; Mangelson, Joshua G; Rodriguez, Alberto; Kaess, Michael; ... Show more Show less
DownloadSubmitted version (6.771Mb)
Open Access Policy
Open Access Policy
Creative Commons Attribution-Noncommercial-Share Alike
Terms of use
Metadata
Show full item recordAbstract
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.
Date issued
2021Department
Massachusetts Institute of Technology. Department of Mechanical EngineeringJournal
2021 IEEE International Conference on Robotics and Automation (ICRA)
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Suresh, Sudharshan, Bauza, Maria, Yu, Kuan-Ting, Mangelson, Joshua G, Rodriguez, Alberto et al. 2021. "Tactile SLAM: Real-time inference of shape and pose from planar pushing." 2021 IEEE International Conference on Robotics and Automation (ICRA).
Version: Original manuscript