Augmenting physics simulators with neural networks for model learning and control

Physics simulators play an important role in robot state estimation, planning and control; however, many real-world control problems involve complex contact dynamics that cannot be characterized analytically. Therefore, most physics simulators employ approximations that lead to a loss in precision. We propose a hybrid dynamics model, combining a deterministic physical simulator with a stochastic neural network for dynamics modeling as it provides us with expressiveness, efficiency, and generalizability simultaneously. To demonstrate this, we compare our hybrid model to both purely analytical models and purely learned models. We then show that our model is able to characterize the complex distribution of object trajectories and compare it with existing methods. We further build in object based representation into the neural network so that our hybrid model can generalize across number of objects. Finally, we use our hybrid model to complete complex control tasks in simulation and on a real robot and show that our model generalizes to novel environments with varying object shapes and materials.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2019

Author dc:creator

• Ajay, Anurag.

Advisor dc:contributor.advisor

• Leslie P. Kaelbling and Joshua B. Tenenbaum.

• Electrical Engineering and Computer Science.

Statement dc:rights

• MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/122747

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/122747