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@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={short video description;
10-minute talk
},
}