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参与实验室科研项目
人机协同柔性智造关键技术与集成验证
学术成果
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Conference Articles
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AdvGrasp: Adversarial Attacks on Robotic Grasping from a Physical Perspective
Xiaofei Wang,
and Yun-Bo Zhao
In IJCAI-25
2025
[Abs]
[pdf]
Adversarial attacks on robotic grasping provide valuable insights into evaluating and improving the robustness of these systems. Unlike studies that focus solely on neural network predictions while overlooking the physical principles of grasping, this paper introduces AdvGrasp, a framework for adversarial attacks on robotic grasping from a physical perspective. Specifically, AdvGrasp targets two core aspects lift capability, which evaluates the ability to lift objects against gravity, and grasp stability, which assesses resistance to external disturbances. By deforming the object’s shape to increase gravitational torque and reduce stability margin in the wrench space, our method systematically degrades these two key grasping metrics, generating adversarial objects that compromise grasp performance. Extensive experiments across diverse scenarios validate the effectiveness of AdvGrasp, while real-world validations demonstrate its robustness and practical applicability. Codes and benchmarks will be released upon paper acceptance.
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