[Submitted on 26 Oct 2026]
Title:Program-Level Adversarial Co-Adaptive Self-Play for Robotic Security
View a PDF of the paper titled Program-Level Adversarial Co-Adaptive Self-Play for Robotic Security, by Leo Benaharon and 4 other authors
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Abstract:Coding agents can adapt robotic software by revising executable programs, but successful responses to an evolving opponent may not remain effective. We present a framework for program-level adversarial co-adaptive self-play in which coding agents generate attacks and defenses from tested opponent code and mission feedback, without model fine-tuning. Environment-specific interfaces support external programs for autonomous driving and SDK-level defenses for humanoid locomotion. Two eight-generation case studies reveal concrete software responses: a driving defender introduces direct-actuation recovery, while a humanoid defender revises incoming-state validation. These responses provide local task recovery but do not establish sustained robustness. The framework makes such adaptation and limitations observable through executable artifacts and task-level evidence.
Comments: 8 pages, 5 figures
Subjects:
Robotics (cs.RO)
Cite as: arXiv:2610.36143 [cs.RO]
(or arXiv:2610.36143v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2610.36143
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Chi Zhang [view email] [v1] Mon, 26 Oct 2026 18:49:21 UTC (2,501 KB)
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