论文标题
对基于优化的计划者的对抗性攻击
Adversarial Attacks on Optimization based Planners
论文作者
论文摘要
轨迹计划是机器人算法架构中的关键作品。轨迹规划师通常使用迭代优化方案来生成平滑轨迹，以避免碰撞，并且在机器人的物理规格下，可以追踪碰撞，并且是最佳跟踪的。从最初的估计开始，计划者迭代地完善解决方案，以满足所需的约束。在本文中，我们表明，基于迭代优化的计划者可能容易受到迫使计划者完全失败或大大增加找到解决方案所需的时间的对抗性攻击。这里的关键见解是环境中的对手可以直接影响计划者的优化成本函数。我们演示了对手如何调整自己的状态配置，从而导致目标的条件较差，从而导致失败。我们将我们的方法应用于两个最先进的轨迹规划师，并证明对手可以始终如一地利用迭代优化方案的某些弱点。
Trajectory planning is a key piece in the algorithmic architecture of a robot. Trajectory planners typically use iterative optimization schemes for generating smooth trajectories that avoid collisions and are optimal for tracking given the robot's physical specifications. Starting from an initial estimate, the planners iteratively refine the solution so as to satisfy the desired constraints. In this paper, we show that such iterative optimization based planners can be vulnerable to adversarial attacks that force the planner either to fail completely, or significantly increase the time required to find a solution. The key insight here is that an adversary in the environment can directly affect the optimization cost function of a planner. We demonstrate how the adversary can adjust its own state configurations to result in poorly conditioned eigenstructure of the objective leading to failures. We apply our method against two state of the art trajectory planners and demonstrate that an adversary can consistently exploit certain weaknesses of an iterative optimization scheme.
