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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
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.
},
wwwnote={<a href="https://www.youtube.com/watch?v=rrcj_oovzWE">short video description},
}
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