Accurate Vision-based Manipulation through Contact Reasoning
Name
1911.03112.pdf
Description
Accepted version
Size
1.9 MB
Format
Unknown
Checksum (MD5)
98ad30f6078a9be4d37726c469009f3f
Author(s)
Kloss, Alina
Bauza Villalonga, Maria
Wu, Jiajun
Tenenbaum, Joshua B
Rodriguez Garcia, Alberto
Bohg, Jeannette
Date Issued
April 2020
Journal
Proceedings - IEEE International Conference on Robotics and Automation
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Kloss, Alina, Bauza, Maria, Wu, Jiajun, Tenenbaum, Joshua B, Rodriguez, Alberto et al. 2020. "Accurate Vision-based Manipulation through Contact Reasoning." Proceedings - IEEE International Conference on Robotics and Automation.
Version
Author's final manuscript
Abstract
© 2020 IEEE. Planning contact interactions is one of the core challenges of many robotic tasks. Optimizing contact locations while taking dynamics into account is computationally costly and, in environments that are only partially observable, executing contact-based tasks often suffers from low accuracy. We present an approach that addresses these two challenges for the problem of vision-based manipulation. First, we propose to disentangle contact from motion optimization. Thereby, we improve planning efficiency by focusing computation on promising contact locations. Second, we use a hybrid approach for perception and state estimation that combines neural networks with a physically meaningful state representation. In simulation and real-world experiments on the task of planar pushing, we show that our method is more efficient and achieves a higher manipulation accuracy than previous vision-based approaches.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
MIT-IBM Watson AI Lab
Massachusetts Institute of Technology. Department of Mechanical Engineering
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/ICRA40945.2020.9197409
