cs.RO提交于 2026-10-28已译

面向机器人安全的程序级对抗共适应自博弈

Program-Level Adversarial Co-Adaptive Self-Play for Robotic Security

Leo Benaharon · Chi Zhang · Wei Zheng · Weifan Zhang · Yifan Wei

中文摘要

编码智能体通过修订可执行程序来适应机器人软件，但针对演化对手的响应可能失效。该框架实现程序级对抗共适应自博弈，编码智能体基于测试过的对手代码和任务反馈生成攻防策略，无需模型微调。环境特定接口支持自动驾驶外部程序及人形机器人SDK级防御。两项八代案例研究显示具体软件响应：驾驶防御者引入直接执行恢复，人形防御者修订输入状态验证。这些响应提供局部任务恢复，但未建立持续鲁棒性。框架通过可执行工件和任务级证据使适应过程与局限性可观测。

关键要点

01提出无需微调的程序级对抗共适应自博弈框架，基于对手代码与任务反馈生成攻防策略。
02支持自动驾驶外部程序接口及人形机器人SDK级防御，实现环境特定的软件适配。
03八代案例研究显示防御者引入直接执行恢复及输入状态验证修订，实现局部任务恢复。
04生成的软件响应仅提供局部恢复能力，未能建立针对演化对手的持续鲁棒性。

解读

尚无解读。

原始英文摘要

arXiv:2610.36143v1 Announce Type: new 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.