6 accepted papers
Collaborative perception (CP) enables multiple vehicles to augment their individual perception capacities through the exchange of feature-level sensory data. However, this fusion mechanism is inherently vulnerable to adversarial attacks, especially in fully untrusted-vehicle environments. Existing d
Hybrid action space, which combines discrete choices and continuous parameters, is prevalent in domains such as robot control and game AI. However, efficiently modeling and optimizing hybrid discrete-continuous action space remains a fundamental challenge, mainly due to limited policy expressiveness
Collaborative perception (CP) significantly extends the sensing range of connected and autonomous vehicles (CAVs). However, its reliance on data fusion among multiple CAVs makes it inherently vulnerable to adversarial attacks from malicious participants. Existing defenses primarily rely on output-le…
Trajectory prediction plays a crucial role in autonomous driving systems, and exploring its vulnerability has garnered widespread attention. However, existing trajectory prediction attack methods often rely on single-point attacks to make efficient perturbations. This limits their applications in re…
The 6D pose estimation for metal parts is essential in industrial robotic applications. The color homogeneity, texture-less and light-reflecting properties of metal parts raise great challenges. Current 6D pose estimation methods have gained extensive concern using CNNs. However, these CNN-based met
Extensive research has been conducted on assessing grasp stability, a crucial prerequisite for achieving optimal grasping strategies, including the minimum force grasping policy. However, existing works employ basic feature-level fusion techniques to combine visual and tactile modalities, resulting…