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---
-active: true
+active: false
kerberos: queeney
name: Jimmy Queeney
office: 31-248F
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+---
+title: Robust Dynamics Generalization in Deep RL
+subtitle:
+date: 2025-06-10
+description: Deep RL framework for adaptive in-distribution and robust out-of-distribution dynamics generalization
+summary:
+authors:
+- queeney
+- xyc
+featured_image: '/images/projects/gram_thumbnail.png'
+papers:
+- queeney2024gram
+active: true
+---
+
+
+
+### About
+
+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.
+
+### Links
+
+* [Paper](https://arxiv.org/abs/2412.04323)
+* [Code](https://github.com/merlresearch/gram)
+
+### Videos
+
+
+
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+
+### About
+
+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.
+
+### Links
+
+* [Paper](https://arxiv.org/abs/2412.04323)
+* [Code](https://github.com/merlresearch/gram)
+
+### Videos
+
+
+
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