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Found 10 papers, 2 papers with code
Estimating Model Uncertainty of Neural Network in Sparse Information Form
As a result, we show that the information form of MND can be scalably applied to represent model uncertainty in MND.
Topology-Matching Normalizing Flows for Out-of-Distribution Detection in Robot Learning
To facilitate reliable deployments of autonomous robots in the real world, Out-of-Distribution (OOD) detection capabilities are often required.
Density-based Feasibility Learning with Normalizing Flows for Introspective Robotic Assembly
Machine Learning (ML) models in Robotic Assembly Sequence Planning (RASP) need to be introspective on the predicted solutions, i. e. whether they are feasible or not, to circumvent potential efficiency degradation.
Efficient and Feasible Robotic Assembly Sequence Planning via Graph Representation Learning
With GRACE, we are able to extract meaningful information from the graph input and predict assembly sequences in a step-by-step manner.
Virtual Reality via Object Pose Estimation and Active Learning: Realizing Telepresence Robots with Aerial Manipulation Capabilities
This article presents a novel telepresence system for advancing aerial manipulation in dynamic and unstructured environments.
Introspective Robot Perception using Smoothed Predictions from Bayesian Neural Networks
This work focuses on improving uncertainty estimation in the field of object classification from RGB images and demonstrates its benefits in two robotic applications.
Bayesian Active Learning for Sim-to-Real Robotic Perception
While learning from synthetic training data has recently gained an increased attention, in real-world robotic applications, there are still performance deficiencies due to the so-called Sim-to-Real gap.
Trust Your Robots! Predictive Uncertainty Estimation of Neural Networks with Sparse Gaussian Processes
This paper presents a probabilistic framework to obtain both reliable and fast uncertainty estimates for predictions with Deep Neural Networks (DNNs).
A Survey of Uncertainty in Deep Neural Networks
Different examples from the wide spectrum of challenges in different fields give an idea of the needs and challenges regarding uncertainties in practical applications.
Estimating Model Uncertainty of Neural Networks in Sparse Information Form
We present a sparse representation of model uncertainty for Deep Neural Networks (DNNs) where the parameter posterior is approximated with an inverse formulation of the Multivariate Normal Distribution (MND), also known as the information form.
