
Learning to Plan Optimally with Flowbased Motion Planner
Samplingbased motion planning is the predominant paradigm in many real...
read it

MultiObjective Bayesian Optimisation and Joint Inversion for Active Sensor Fusion
A critical decision process in data acquisition for mineral and energy r...
read it

Sparse Spectrum Warped Input Measures for Nonstationary Kernel Learning
We establish a general form of explicit, inputdependent, measurevalued...
read it

Heteroscedastic Bayesian Optimisation for Stochastic Model Predictive Control
Model predictive control (MPC) has been successful in applications invol...
read it

Online Domain Adaptation for Occupancy Mapping
Creating accurate spatial representations that take into account uncerta...
read it

Euclideanizing Flows: Diffeomorphic Reduction for Learning Stable Dynamical Systems
Robotic tasks often require motions with complex geometric structures. W...
read it

Guided UncertaintyAware Policy Optimization: Combining Learning and ModelBased Strategies for SampleEfficient Policy Learning
Traditional robotic approaches rely on an accurate model of the environm...
read it

Estimating Motion Uncertainty with Bayesian ICP
Accurate uncertainty estimation associated with the pose transformation ...
read it

Intrinsic Exploration as MultiObjective RL
Intrinsic motivation enables reinforcement learning (RL) agents to explo...
read it

Inferring the Material Properties of Granular Media for Robotic Tasks
Granular media (e.g., cereal grains, plastic resin pellets, and pills) a...
read it

DISCO: Double Likelihoodfree Inference Stochastic Control
Accurate simulation of complex physical systems enables the development,...
read it

Reinforcement Learning with Probabilistically Complete Exploration
Balancing exploration and exploitation remains a key challenge in reinfo...
read it

Semisupervised Learning Approach to Generate Neuroimaging Modalities with Adversarial Training
Magnetic Resonance Imaging (MRI) of the brain can come in the form of di...
read it

Dynamic Hilbert Maps: RealTime Occupancy Predictions in Changing Environment
This paper addresses the problem of learning instantaneous occupancy lev...
read it

Bayesian Curiosity for Efficient Exploration in Reinforcement Learning
Balancing exploration and exploitation is a fundamental part of reinforc...
read it

IRIS: Implicit Reinforcement without Interaction at Scale for Learning Control from Offline Robot Manipulation Data
Learning from offline task demonstrations is a problem of great interest...
read it

OCTNet: Trajectory Generation in New Environments from Past Experiences
Being able to safely operate for extended periods of time in dynamic env...
read it

Local Samplingbased Planning with Sequential Bayesian Updates
Samplingbased planners are the predominant motion planning paradigm for...
read it

OccTraj120: Occupancy Maps with Associated Trajectories
Trajectory modelling had been the principal research area for understand...
read it

Speeding Up Iterative Closest Point Using Stochastic Gradient Descent
Sensors producing 3D point clouds such as 3D laser scanners and RGBD ca...
read it

Kernel Trajectory Maps for MultiModal Probabilistic Motion Prediction
Understanding the dynamics of an environment, such as the movement of hu...
read it

Learning to Plan Hierarchically from Curriculum
We present a framework for learning to plan hierarchically in domains wi...
read it

BayesSim: adaptive domain randomization via probabilistic inference for robotics simulators
We introduce BayesSim, a framework for robotics simulations allowing a f...
read it

Bayesian Deconditional Kernel Mean Embeddings
Conditional kernel mean embeddings form an attractive nonparametric fram...
read it

Bayesian Learning of Conditional Kernel Mean Embeddings for Automatic LikelihoodFree Inference
In likelihoodfree settings where likelihood evaluations are intractable...
read it

Bayesian optimisation under uncertain inputs
Bayesian optimisation (BO) has been a successful approach to optimise fu...
read it

Balancing Global Exploration and Localconnectivity Exploitation with Rapidlyexploring Random disjointedTrees
Sampling efficiency in a highly constrained environment has long been a ...
read it

Directional grid maps: modeling multimodal angular uncertainty in dynamic environments
Robots often have to deal with the challenges of operating in dynamic an...
read it

Hyperparameter Learning for Conditional Mean Embeddings with Rademacher Complexity Bounds
Conditional mean embeddings are nonparametric models that encode conditi...
read it

CycleConsistent Adversarial Learning as Approximate Bayesian Inference
We formalize the problem of learning interdomain correspondences in the ...
read it

Index Set Fourier Series Features for Approximating Multidimensional Periodic Kernels
Periodicity is often studied in timeseries modelling with autoregressive...
read it

Functional Path Optimisation for Exploration in Continuous Occupancy Maps
Autonomous exploration is a complex task where the robot moves through a...
read it

Learning NonStationary SpaceTime Models for Environmental Monitoring
One of the primary aspects of sustainable development involves accurate ...
read it

Adaptive Sensing for Learning Nonstationary Environment Models
Most environmental phenomena, such as wind profiles, ozone concentration...
read it

Removing scanner biases using Generative Adversarial Networks
Magnetic Resonance Imaging (MRI) of the brain has been used to investiga...
read it

Learning to Race through Coordinate Descent Bayesian Optimisation
In the automation of many kinds of processes, the observable outcome can...
read it

Learning to Navigate by Growing Deep Networks
Adaptability is central to autonomy. Intuitively, for highdimensional l...
read it

Bayesian Optimisation for Safe Navigation under Localisation Uncertainty
In outdoor environments, mobile robots are required to navigate through ...
read it

Urban Scene Segmentation with LaserConstrained CRFs
Robots typically possess sensors of different modalities, such as colour...
read it

Online Adaptation of Deep Architectures with Reinforcement Learning
Online learning has become crucial to many problems in machine learning....
read it

Simple Online and Realtime Tracking
This paper explores a pragmatic approach to multiple object tracking whe...
read it

Expected Similarity Estimation for LargeScale Batch and Streaming Anomaly Detection
We present a novel algorithm for anomaly detection on very large dataset...
read it

Distributed Anytime MAP Inference
We present a distributed anytime algorithm for performing MAP inference ...
read it
Fabio Ramos
is this you? claim profile