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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.
Generalization to unseen environments is a significant challenge in the field ofrobotics and control. In this work, we focus on contextual reinforcementlearning, where agents act within environments with varying contexts, such asself-driving cars or quadrupedal robots that need to operate in differentterrains or weather conditions than they were trained for. We tackle the criticaltask of generalizing to out-of-distribution (OOD) settings, without access toexplicit context information at test time. Recent work has addressed this problemby training a context encoder and a history adaptation module in separate stages.While promising, this two-phase approach is cumbersome to implement and train. Wesimplify the methodology and introduce SPARC: single-phase adaptation for robustcontrol. We test SPARC on varying contexts within the high-fidelity racingsimulator Gran Turismo 7 and wind-perturbed MuJoCo environments, and find that itachieves 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.
},
}
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