GRAM: Generalization in Deep RL With a Robust Adaptation Module
James Queeney, Xiaoyi Cai, Alexander Schperberg, Radu Corcodel, Mouhacine Benosman, Jonathan P. How
Abstract
The reliable deployment of deep reinforcement learning in real-world settings requires the ability to generalize across a variety of conditions, including both in-distribution scenarios seen during training as well as novel out-of-distribution scenarios. In this work, we present a framework for dynamics generalization in deep reinforcement learning that unifies these two distinct types of generalization within a single architecture. We introduce a robust adaptation module that provides a mechanism for identifying and reacting to both in-distribution and out-of-distribution environment dynamics, along with a joint training pipeline that combines the goals of in-distribution adaptation and out-of-distribution robustness. Our algorithm GRAM achieves strong generalization performance across in-distribution and out-of-distribution scenarios upon deployment, which we demonstrate through extensive simulation and hardware locomotion experiments on a quadruped robot.
BibTeX
@inproceedings{ral2026_gramgeneralizati,
title = {GRAM: Generalization in Deep RL With a Robust Adaptation Module},
author = {James Queeney and Xiaoyi Cai and Alexander Schperberg and Radu Corcodel and Mouhacine Benosman and Jonathan P. How},
booktitle = {RA-L 2026},
year = {2026}
}