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Out-of-Distribution Generalization with a SPARC: Racing 100 Unseen Vehicles with a Single Policy.
Bram
Grooten, Patrick MacAlpine, Kaushik Subramanian, Peter
Stone, and Peter R. Wurman.
In The AAAI Conference on Artificial Intelligence,
January 2026.
short video description;
10-minute talk
Generalization to unseen environments is a significant challenge in the field of robotics and control. In this work, we focus on contextual reinforcement learning, where agents act within environments with varying contexts, such as self-driving cars or quadrupedal robots that need to operate in different terrains or weather conditions than they were trained for. We tackle the critical task of generalizing to out-of-distribution (OOD) settings, without access to explicit context information at test time. Recent work has addressed this problem by training a context encoder and a history adaptation module in separate stages. While promising, this two-phase approach is cumbersome to implement and train. We simplify the methodology and introduce SPARC: single-phase adaptation for robust control. We test SPARC on varying contexts within the high-fidelity racing simulator Gran Turismo 7 and wind-perturbed MuJoCo environments, and find that it achieves reliable and robust OOD generalization.
@InProceedings{SPARC_aaai2026,
author = {Bram Grooten and Patrick MacAlpine and Kaushik Subramanian and Peter Stone and Peter R. Wurman},
title = {Out-of-Distribution Generalization with a SPARC: Racing 100 Unseen Vehicles with a Single Policy},
booktitle = {The AAAI Conference on Artificial Intelligence},
year = {2026},
month = {January},
location = {Singapore, Singapore},
abstract = {Generalization to unseen environments is a significant challenge in the field of robotics and control. In this work, we focus on contextual reinforcement learning, where agents act within environments with varying contexts, such as self-driving cars or quadrupedal robots that need to operate in different terrains or weather conditions than they were trained for. We tackle the critical task of generalizing to out-of-distribution (OOD) settings, without access to explicit context information at test time. Recent work has addressed this problem by training a context encoder and a history adaptation module in separate stages. While promising, this two-phase approach is cumbersome to implement and train. We simplify the methodology and introduce SPARC: single-phase adaptation for robust control. We test SPARC on varying contexts within the high-fidelity racing simulator Gran Turismo 7 and wind-perturbed MuJoCo environments, and find that it achieves reliable and robust OOD generalization.},
wwwnote={<a href="https://www.youtube.com/watch?v=rrcj_oovzWE" target="_blank">short video description</a>;
<a href="https://www.youtube.com/watch?v=gRp62ZG3Xic" target="_blank">10-minute talk</a>
},
}
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