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08/08/2023
Path Signatures for Diversity in Probabilistic Trajectory Optimisation
Motion planning can be cast as a trajectory optimisation problem where a...
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06/06/2023
Learning to Simulate Tree-Branch Dynamics for Manipulation
We propose to use a simulation driven inverse inference approach to mode...
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05/26/2023
IndustReal: Transferring Contact-Rich Assembly Tasks from Simulation to Reality
Robotic assembly is a longstanding challenge, requiring contact-rich int...
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03/28/2023
DefGraspNets: Grasp Planning on 3D Fields with Graph Neural Nets
Robotic grasping of 3D deformable objects is critical for real-world app...
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09/22/2022
Batch Bayesian optimisation via density-ratio estimation with guarantees
Bayesian optimisation (BO) algorithms have shown remarkable success in a...
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07/03/2022
Renaissance Robot: Optimal Transport Policy Fusion for Learning Diverse Skills
Deep reinforcement learning (RL) is a promising approach to solving comp...
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04/14/2022
Accelerated Policy Learning with Parallel Differentiable Simulation
Deep reinforcement learning can generate complex control policies, but r...
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03/19/2022
DiSECt: A Differentiable Simulator for Parameter Inference and Control in Robotic Cutting
Robotic cutting of soft materials is critical for applications such as f...
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03/13/2022
Adaptive Model Predictive Control by Learning Classifiers
Stochastic model predictive control has been a successful and robust con...
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03/02/2022
L4KDE: Learning for KinoDynamic Tree Expansion
We present the Learning for KinoDynamic Tree Expansion (L4KDE) method fo...
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03/01/2022
Bayesian Optimisation for Robust Model Predictive Control under Model Parameter Uncertainty
We propose an adaptive optimisation approach for tuning stochastic model...
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02/09/2022
Stein Particle Filter for Nonlinear, Non-Gaussian State Estimation
Estimation of a dynamical system's latent state subject to sensor noise ...
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12/09/2021
A Bayesian Treatment of Real-to-Sim for Deformable Object Manipulation
Deformable object manipulation remains a challenging task in robotics re...
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11/01/2021
Robot Learning from Randomized Simulations: A Review
The rise of deep learning has caused a paradigm shift in robotics resear...
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09/21/2021
Rapid Replanning in Consecutive Pick-and-Place Tasks with Lazy Experience Graph
In an environment where a manipulator needs to execute multiple pick-and...
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09/18/2021
Probabilistic Inference of Simulation Parameters via Parallel Differentiable Simulation
To accurately reproduce measurements from the real world, simulators nee...
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08/26/2021
Parallelised Diffeomorphic Sampling-based Motion Planning
We propose Parallelised Diffeomorphic Sampling-based Motion Planning (PD...
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07/09/2021
BayesSimIG: Scalable Parameter Inference for Adaptive Domain Randomization with IsaacGym
BayesSim is a statistical technique for domain randomization in reinforc...
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07/09/2021
Probabilistic Trajectory Prediction with Structural Constraints
This work addresses the problem of predicting the motion trajectories of...
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07/04/2021
Learning ODEs via Diffeomorphisms for Fast and Robust Integration
Advances in differentiable numerical integrators have enabled the use of...
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06/07/2021
Stein ICP for Uncertainty Estimation in Point Cloud Matching
Quantification of uncertainty in point cloud matching is critical in man...
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05/25/2021
DiSECt: A Differentiable Simulation Engine for Autonomous Robotic Cutting
Robotic cutting of soft materials is critical for applications such as f...
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04/28/2021
Fast Joint Space Model-Predictive Control for Reactive Manipulation
Sampling-based model predictive control (MPC) is a promising tool for fe...
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03/23/2021
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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02/17/2021
BORE: Bayesian Optimization by Density-Ratio Estimation
Bayesian optimization (BO) is among the most effective and widely-used b...
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11/17/2020
A User's Guide to Calibrating Robotics Simulators
Simulators are a critical component of modern robotics research. Strateg...
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11/16/2020
Fast Uncertainty Quantification for Deep Object Pose Estimation
Deep learning-based object pose estimators are often unreliable and over...
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11/15/2020
Stein Variational Model Predictive Control
Decision making under uncertainty is critical to real-world, autonomous ...
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11/12/2020
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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11/05/2020
STReSSD: Sim-To-Real from Sound for Stochastic Dynamics
Sound is an information-rich medium that captures dynamic physical event...
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10/21/2020
Learning to Plan Optimally with Flow-based Motion Planner
Sampling-based motion planning is the predominant paradigm in many real-...
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10/12/2020
Multi-Objective 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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10/09/2020
Sparse Spectrum Warped Input Measures for Nonstationary Kernel Learning
We establish a general form of explicit, input-dependent, measure-valued...
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10/01/2020
Heteroscedastic Bayesian Optimisation for Stochastic Model Predictive Control
Model predictive control (MPC) has been successful in applications invol...
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07/01/2020
Online Domain Adaptation for Occupancy Mapping
Creating accurate spatial representations that take into account uncerta...
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05/27/2020
Euclideanizing Flows: Diffeomorphic Reduction for Learning Stable Dynamical Systems
Robotic tasks often require motions with complex geometric structures. W...
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05/21/2020
Guided Uncertainty-Aware Policy Optimization: Combining Learning and Model-Based Strategies for Sample-Efficient Policy Learning
Traditional robotic approaches rely on an accurate model of the environm...
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04/16/2020
Estimating Motion Uncertainty with Bayesian ICP
Accurate uncertainty estimation associated with the pose transformation ...
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04/06/2020
Intrinsic Exploration as Multi-Objective RL
Intrinsic motivation enables reinforcement learning (RL) agents to explo...
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03/18/2020
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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02/18/2020
DISCO: Double Likelihood-free Inference Stochastic Control
Accurate simulation of complex physical systems enables the development,...
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01/20/2020
Reinforcement Learning with Probabilistically Complete Exploration
Balancing exploration and exploitation remains a key challenge in reinfo...
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12/09/2019
Semi-supervised 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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12/04/2019
Dynamic Hilbert Maps: Real-Time Occupancy Predictions in Changing Environment
This paper addresses the problem of learning instantaneous occupancy lev...
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11/20/2019
Bayesian Curiosity for Efficient Exploration in Reinforcement Learning
Balancing exploration and exploitation is a fundamental part of reinforc...
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11/13/2019
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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09/25/2019
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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09/08/2019
Local Sampling-based Planning with Sequential Bayesian Updates
Sampling-based planners are the predominant motion planning paradigm for...
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09/05/2019
Occ-Traj120: Occupancy Maps with Associated Trajectories
Trajectory modelling had been the principal research area for understand...
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07/22/2019