Disney Research


Simulation representations of robots have advanced in recent years. Yet, there remain significant sim-to-real gaps because of modeling assumptions and hard-to-model behaviors such as friction.

We propose to augment common simulation representations with a transformer-inspired architecture, by training a network to predict the true state of robot building blocks given their simulation state. Because we augment building blocks, rather than the full simulation state, we make our approach modular which improves generalizability and robustness.

We use our neural network to augment the state of robot actuators, and also of rigid body states. Our actuator augmentation generalizes well across robots, and our rigid body augmentation, 0results in improvements even under high uncertainty in model parameters.

Additional Content

Copyright Notice

The documents contained in these directories are included by the contributing authors as a means to ensure timely dissemination of scholarly and technical work on a non-commercial basis. Copyright and all rights therein are maintained by the authors or by other copyright holders, notwithstanding that they have offered their works here electronically. It is understood that all persons copying this information will adhere to the terms and constraints invoked by each author’s copyright. These works may not be reposted without the explicit permission of the copyright holder.