5 accepted papers
Learning the dynamic causal structure is a difficult challenge in discovering causality from time series. Most existing studies rely on distributional or structural invariance to uncover the underlying causal dynamics, assuming stationary or partially stationary causality, which frequently conflicts…
Fabric-based pneumatic actuators (FPAs) with asymmetric chambers offer exceptional lightweight properties and high output force, making them ideal for soft gloves. However, current characterization and modeling methods for FPAs are generally limited, focusing primarily on single flexion chambers wit
Discovering the causality from observational data is a crucial task in various scientific domains. With increasing awareness of privacy, data are not allowed to be exposed, and it is very hard to learn causal graphs from dispersed data, since these data may have different distributions. In this pape…
Learning directed acyclic graph (DAG) that describes the causality of observed data is a very challenging but important task. Due to the limited quantity and quality of observed data, and non-identifiability of causal graph, it is almost impossible to infer a single precise DAG. Some methods approx…
Fabric-based pneumatic actuators (FPAs) are promising in the design of soft wearable assistive gloves, owing to the lightweight and compliant advantages. However, the existing FPAs generally suffer from small output force, limiting users' applications. This letter presents a new class of high-force