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Learning Dynamic Tasks on a Large-scale Soft Robot in a Handful of Trials
Unlike traditional rigid robots, soft robots offer more flexibility, compliance, and adaptability. They are also typically cheaper to …
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Gaussian Processes on Cellular Complexes
In recent years, there has been considerable interest in developing machine learning models on graphs to account for topological …
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Gaussian Processes on Cellular Complexes
Iterated INLA for State and Parameter Estimation in Nonlinear Dynamical Systems
Data assimilation (DA) methods use priors arising from differential equations to robustly interpolate and extrapolate data. Popular …
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A Unifying Variational Framework for Gaussian Process Motion Planning
To control how a robot moves, motion planning algorithms must compute paths in high-dimensional state spaces while accounting for …
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A Unifying Variational Framework for Gaussian Process Motion Planning
Interpretable Deep Gaussian Processes for Geospatial Tasks
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Scalable Data Assimilation with Message Passing
Data assimilation is a core component of numerical weather prediction systems. The large quantity of data processed during assimilation …
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Thin and Deep Gaussian Processes
Gaussian processes (GPs) can provide a principled approach to uncertainty quantification with easy-to-interpret kernel hyperparameters, …
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Thin and Deep Gaussian Processes
Neural Field Movement Primitives for Joint Modelling of Scenes and Motions
This paper presents a novel Learning from Demonstration (LfD) method that uses neural fields to learn new skills efficiently and …
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Sliding Touch-based Exploration for Modeling Unknown Object Shape with Multi-finger Hands
Efficient and accurate 3D object shape reconstruction contributes significantly to the success of a robot’s physical interaction …
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Safe Trajectory Sampling in Model-based Reinforcement Learning
Model-based reinforcement learning aims to learn a policy to solve a target task by leveraging a learned dynamics model. This approach, …
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Safe Trajectory Sampling in Model-based Reinforcement Learning