5 accepted papers
Anomaly detection (AD) under data contamination is critical for deploying unsupervised defect detection in industrial environments, where curating perfectly clean training sets is impractical. However, existing methods are sensitive to contamination, suffering significant performance degradation as …
We aim to solve unsupervised anomaly detection in a practical challenging environment where the normal dataset is both contaminated with defective regions and its product class distribution is tailed but unknown. We observe that existing models suffer from tail-versus-noise trade-off where if a mode…
This paper presents an approach to learn and generalize robotic skills from a demonstration using deep reinforcement learning (deep RL). Dynamic Movement Primitives (DMPs) formulate a nonlinear differential equation and produce the observed movement from a demonstration. However, it is hard to gener…
Flapping-wing micro air vehicles (FWMAVs) become promising research platforms due to their advantages such as various maneuverability, and concealment. However, unsteady flow at low Reynolds number around the wings makes their dynamics time-varying and highly non-linear. It makes autonomous flight o…
This paper describes a vision-based target tracking method for a skid-steer vehicle. With the development of deep reinforcement learning, many researchers have tried to generate an end-to-end policy to control the mobile robot from a raw pixel image data. However, the action in most research only co…