Adversarial Body Shape Search for Legged Robots
- URL: http://arxiv.org/abs/2205.10187v1
- Date: Fri, 20 May 2022 13:55:47 GMT
- Title: Adversarial Body Shape Search for Legged Robots
- Authors: Takaaki Azakami, Hiroshi Kera, Kazuhiko Kawamoto
- Abstract summary: We propose an evolutionary computation method for an adversarial attack on the length and thickness of parts of legged robots.
Finding adversarial body shape can be used to proactively diagnose the vulnerability of legged robot walking.
- Score: 3.480626767752489
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
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