4 accepted papers
Continuum robots have long held a great potential for applications in inspection of remote, hard-to-reach environments. In future environments such as the Deep Space Gateway, remote deployment of robotic solutions will require a high level of autonomy due to communication delays and unavailability o…
We propose a novel Bayesian nonparametric method to learn translation-invariant relationships on non-Euclidean domains. The resulting graph convolutional Gaussian processes can be applied to problems in machine learning for which the input observations are functions with domains on general graphs. T…
A reliable, accurate, and yet simple dynamic model is important to analyze, design and control continuum manipulators. Such models should be fast, as simple as possible and user-friendly to be widely accepted by the ever-growing robotics research community. In this study, we introduce two new modeli…
We present a novel cost function for semi-supervised learning of neural networks that encourages compact clustering of the latent space to facilitate separation. The key idea is to dynamically create a graph over embeddings of labeled and unlabeled samples of a training batch to capture underlying s…