
Dual Online Stein Variational Inference for Control and Dynamics
Model predictive control (MPC) schemes have a proven track record for de...
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BORE: Bayesian Optimization by DensityRatio Estimation
Bayesian optimization (BO) is among the most effective and widelyused b...
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A User's Guide to Calibrating Robotics Simulators
Simulators are a critical component of modern robotics research. Strateg...
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Fast Uncertainty Quantification for Deep Object Pose Estimation
Deep learningbased object pose estimators are often unreliable and over...
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Stein Variational Model Predictive Control
Decision making under uncertainty is critical to realworld, autonomous ...
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Anticipatory Navigation in Crowds by Probabilistic Prediction of Pedestrian Future Movements
Critical for the coexistence of humans and robots in dynamic environment...
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STReSSD: SimToReal from Sound for Stochastic Dynamics
Sound is an informationrich medium that captures dynamic physical event...
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Learning to Plan Optimally with Flowbased Motion Planner
Samplingbased motion planning is the predominant paradigm in many real...
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MultiObjective Bayesian Optimisation and Joint Inversion for Active Sensor Fusion
A critical decision process in data acquisition for mineral and energy r...
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Sparse Spectrum Warped Input Measures for Nonstationary Kernel Learning
We establish a general form of explicit, inputdependent, measurevalued...
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Heteroscedastic Bayesian Optimisation for Stochastic Model Predictive Control
Model predictive control (MPC) has been successful in applications invol...
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Online Domain Adaptation for Occupancy Mapping
Creating accurate spatial representations that take into account uncerta...
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Euclideanizing Flows: Diffeomorphic Reduction for Learning Stable Dynamical Systems
Robotic tasks often require motions with complex geometric structures. W...
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Guided UncertaintyAware Policy Optimization: Combining Learning and ModelBased Strategies for SampleEfficient Policy Learning
Traditional robotic approaches rely on an accurate model of the environm...
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Estimating Motion Uncertainty with Bayesian ICP
Accurate uncertainty estimation associated with the pose transformation ...
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Intrinsic Exploration as MultiObjective RL
Intrinsic motivation enables reinforcement learning (RL) agents to explo...
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Inferring the Material Properties of Granular Media for Robotic Tasks
Granular media (e.g., cereal grains, plastic resin pellets, and pills) a...
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DISCO: Double Likelihoodfree Inference Stochastic Control
Accurate simulation of complex physical systems enables the development,...
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Reinforcement Learning with Probabilistically Complete Exploration
Balancing exploration and exploitation remains a key challenge in reinfo...
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Semisupervised Learning Approach to Generate Neuroimaging Modalities with Adversarial Training
Magnetic Resonance Imaging (MRI) of the brain can come in the form of di...
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Dynamic Hilbert Maps: RealTime Occupancy Predictions in Changing Environment
This paper addresses the problem of learning instantaneous occupancy lev...
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Bayesian Curiosity for Efficient Exploration in Reinforcement Learning
Balancing exploration and exploitation is a fundamental part of reinforc...
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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...
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OCTNet: Trajectory Generation in New Environments from Past Experiences
Being able to safely operate for extended periods of time in dynamic env...
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Local Samplingbased Planning with Sequential Bayesian Updates
Samplingbased planners are the predominant motion planning paradigm for...
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OccTraj120: Occupancy Maps with Associated Trajectories
Trajectory modelling had been the principal research area for understand...
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Speeding Up Iterative Closest Point Using Stochastic Gradient Descent
Sensors producing 3D point clouds such as 3D laser scanners and RGBD ca...
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Kernel Trajectory Maps for MultiModal Probabilistic Motion Prediction
Understanding the dynamics of an environment, such as the movement of hu...
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Learning to Plan Hierarchically from Curriculum
We present a framework for learning to plan hierarchically in domains wi...
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BayesSim: adaptive domain randomization via probabilistic inference for robotics simulators
We introduce BayesSim, a framework for robotics simulations allowing a f...
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Bayesian Deconditional Kernel Mean Embeddings
Conditional kernel mean embeddings form an attractive nonparametric fram...
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Bayesian Learning of Conditional Kernel Mean Embeddings for Automatic LikelihoodFree Inference
In likelihoodfree settings where likelihood evaluations are intractable...
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Bayesian optimisation under uncertain inputs
Bayesian optimisation (BO) has been a successful approach to optimise fu...
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Balancing Global Exploration and Localconnectivity Exploitation with Rapidlyexploring Random disjointedTrees
Sampling efficiency in a highly constrained environment has long been a ...
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Directional grid maps: modeling multimodal angular uncertainty in dynamic environments
Robots often have to deal with the challenges of operating in dynamic an...
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Hyperparameter Learning for Conditional Mean Embeddings with Rademacher Complexity Bounds
Conditional mean embeddings are nonparametric models that encode conditi...
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CycleConsistent Adversarial Learning as Approximate Bayesian Inference
We formalize the problem of learning interdomain correspondences in the ...
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Index Set Fourier Series Features for Approximating Multidimensional Periodic Kernels
Periodicity is often studied in timeseries modelling with autoregressive...
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Functional Path Optimisation for Exploration in Continuous Occupancy Maps
Autonomous exploration is a complex task where the robot moves through a...
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Learning NonStationary SpaceTime Models for Environmental Monitoring
One of the primary aspects of sustainable development involves accurate ...
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Adaptive Sensing for Learning Nonstationary Environment Models
Most environmental phenomena, such as wind profiles, ozone concentration...
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Removing scanner biases using Generative Adversarial Networks
Magnetic Resonance Imaging (MRI) of the brain has been used to investiga...
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Learning to Race through Coordinate Descent Bayesian Optimisation
In the automation of many kinds of processes, the observable outcome can...
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Learning to Navigate by Growing Deep Networks
Adaptability is central to autonomy. Intuitively, for highdimensional l...
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Bayesian Optimisation for Safe Navigation under Localisation Uncertainty
In outdoor environments, mobile robots are required to navigate through ...
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Urban Scene Segmentation with LaserConstrained CRFs
Robots typically possess sensors of different modalities, such as colour...
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Online Adaptation of Deep Architectures with Reinforcement Learning
Online learning has become crucial to many problems in machine learning....
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Simple Online and Realtime Tracking
This paper explores a pragmatic approach to multiple object tracking whe...
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Expected Similarity Estimation for LargeScale Batch and Streaming Anomaly Detection
We present a novel algorithm for anomaly detection on very large dataset...
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Distributed Anytime MAP Inference
We present a distributed anytime algorithm for performing MAP inference ...
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Fabio Ramos
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