论文标题
对抗身体形状搜索腿部机器人
Adversarial Body Shape Search for Legged Robots
论文作者
论文摘要
我们提出了一种进化计算方法，用于通过深度加固学习对腿部机器人部分的长度和厚度进行对抗性攻击。这种攻击改变了机器人身体的形状，并干扰了步行，我们将攻击的身体称为对抗身体形状。进化计算方法通过最大程度地减少通过步行模拟获得的预期累积奖励来搜索对抗体形状。为了评估所提出方法的有效性，我们在OpenAI体育馆中使用三足机器人Walker2d，ant-V2和Humanoid-V2进行实验。实验结果表明，Walker2D和ANT-V2比身体部位的厚度更容易受到对长度的攻击，而人类V2容易受到对长度和厚度的攻击。我们进一步确定，对抗的身体形状破坏了左右对称性或移动腿部机器人的重心。寻找对抗体形状可用于主动诊断腿部机器人行走的脆弱性。
We propose an evolutionary computation method for an adversarial attack on the length and thickness of parts of legged robots by deep reinforcement learning. This attack changes the robot body shape and interferes with walking-we call the attacked body as adversarial body shape. The evolutionary computation method searches adversarial body shape by minimizing the expected cumulative reward earned through walking simulation. To evaluate the effectiveness of the proposed method, we perform experiments with three-legged robots, Walker2d, Ant-v2, and Humanoid-v2 in OpenAI Gym. The experimental results reveal that Walker2d and Ant-v2 are more vulnerable to the attack on the length than the thickness of the body parts, whereas Humanoid-v2 is vulnerable to the attack on both of the length and thickness. We further identify that the adversarial body shapes break left-right symmetry or shift the center of gravity of the legged robots. Finding adversarial body shape can be used to proactively diagnose the vulnerability of legged robot walking.
