3 accepted papers
Transformers have demonstrated strong performance in time series forecasting, yet they often fail to capture the intrinsic structure of temporal data, making them susceptible to real-world noise and anomalies. Unlike in vision or language, the local geometry of temporal patterns is a critical featur…
Time series forecasting is crucial for applications across multiple domains and various scenarios. Although Transformers have dramatically advanced the landscape of forecasting, their effectiveness remains debated. Recent findings have indicated that simpler linear models might outperform complex Tr…
This paper presents a concept for haptic-based human-in-the-loop aerial manipulation for drilling. The concept serves as a case study for designing the human-drone interface to remotely drill with a mobile-manipulating drone. The notion of the work stems from using drones to perform dangerous tasks…