Combining physical simulators and object-based networks for control
Name
1904.06580.pdf
Description
Submitted version
Size
3.13 MB
Format
Adobe PDF
Checksum (MD5)
e9e12971e55194febf1c1fa0bcabc7bd
Author(s)
Ajay, Anurag.
Bauza Villalonga, Maria
Wu, Jiajun
Fazeli, Nima
Tenenbaum, Joshua B
Rodriguez Garcia, Alberto
Kaelbling, Leslie P
Date Issued
May 2019
Journal
International Conference on Robotics and Automation (ICRA)
Publisher
IEEE
Citation
Ajay, Anurag et al. "Combining physical simulators and object-based networks for control." 2019 International Conference on Robotics and Automation (ICRA 2019), May 20-26, 2019, Montreal, Quebec: 3217-23 doi: 10.1109/ICRA.2019.8794358 ©2019 Author(s)
Version
Original manuscript
Abstract
Physics engines play an important role in robot planning and control; however, many real-world control problems involve complex contact dynamics that cannot be characterized analytically. Most physics engines therefore employ approximations that lead to a loss in precision. In this paper, we propose a hybrid dynamics model, simulator-augmented interaction networks (SAIN), combining a physics engine with an object-based neural network for dynamics modeling. Compared with existing models that are purely analytical or purely data-driven, our hybrid model captures the dynamics of interacting objects in a more accurate and data-efficient manner. Experiments both in simulation and on a real robot suggest that it also leads to better performance when used in complex control tasks. Finally, we show that our model generalizes to novel environments with varying object shapes and materials.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
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/ICRA.2019.8794358
